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This paper presents the overview and rationale behind the Decoder-Side Depth Estimation (DSDE) mode of the MPEG Immersive Video (MIV) standard, using the Geometry Absent profile, for efficient compression of immersive multiview video. A MIV bitstream generated by an encoder operating in the DSDE mode does not include depth maps. It only contains the information required to reconstruct them in the client or in the cloud: decoded views and metadata. The paper explains the technical details and techniques supported by this novel MIV DSDE mode. The description additionally includes the specification on Geometry Assistance Supplemental Enhancement Information which helps to reduce the complexity of depth estimation, when performed in the cloud or at the decoder side. The depth estimation in MIV is a non-normative part of the decoding process, therefore, any method can be used to compute the depth maps. This paper lists a set of requirements for depth estimation, induced by the specific characteristics of the DSDE. The depth estimation reference software, continuously and collaboratively developed with MIV to meet these requirements, is presented in this paper. Several original experimental results are presented. The efficiency of the DSDE is compared to two MIV profiles. The combined non-transmission of depth maps and efficient coding of textures enabled by the DSDE leads to efficient compression and rendering quality improvement compared to the usual encoder-side depth estimation. Moreover, results of the first evaluation of state-of-the-art multiview depth estimators in the DSDE context, including machine learning techniques, are presented. [ABSTRACT FROM AUTHOR]

Image classification needs to consider the existence of image degradations in practice. Although degraded images have various levels of degradation, the degradation levels are usually unknown. This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The proposed network can automatically infer ensemble weights by using estimated degradation levels of degraded images and features of restored images, where the degradation levels are estimated internally. The proposed network is mainly discussed with JPEG distortion, while degradations of both Gaussian noise and blurring are also examined. We demonstrate that the proposed network can classify degraded images over various levels of degradation. This paper also reveals how the image-quality of training data for a classification network affects the classification performance of degraded images. [ABSTRACT FROM AUTHOR]

Continuous and accurate depth information of blast furnace burden surface is important for optimizing charging operations, thereby reducing its energy consumption and CO2 emissions. However, depth estimation for a single image is challenging, especially when estimating the depth of burden surface images in the harsh internal environment of the blast furnace. In this paper, a novel method that is based on edge defocus tracking is proposed to estimate the depth of burden surface images with different morphological characteristics. First, an endoscopic video acquisition system is designed, key frames of burden surface video in stable state are extracted based on feature point optical flow method, and the sparse depth is estimated by using the defocus-based method. Next, the burden surface image is divided into four subregions according to the distribution characteristics of the burden surface, the edge line trajectories and an eight-direction depth gradient template are designed to develop depth propagation rules. Finally, the depth is propagated from edge to the entire image based on edge line tracking method. The experimental results show that the proposed method can accurately and efficiently estimate the depth of the burden surface and provide key data support for optimizing the operation of blast furnace. [ABSTRACT FROM AUTHOR]

In this paper, a data assimilation network is proposed to tackle the challenges of domain generalization for person re-identification (ReID). Most of the existing research efforts only focus on single-dataset issues, and the trained models are difficult to generalize to unseen scenarios. This paper presents a distinctive idea to improve the generality of the model by assimilating three types of images: style-variant images, misaligned images and unlabeled images. The latter two are often ignored in the previous domain generalization ReID studies. In this paper, a non-local convolutional block attention module is designed for assimilating the misaligned images, and an attention adversary network is introduced to correct it. A progressive augmented memory is designed for assimilating the unlabeled images by progressive learning. Moreover, we propose an attention adversary difference loss for attention correction, and a labeling-guide discriminative embedding loss for progressive learning. Rather than designing a specific feature extractor that is robust to style shift as in most previous domain generalization work, we propose a data assimilation meta-learning procedure to train the proposed network, so that it learns to assimilate style-variant images. It is worth mentioning that we add an unlabeled augmented dataset to the source domain to tackle the domain generalization ReID tasks. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art domain generalization methods. [ABSTRACT FROM AUTHOR]

Inter prediction is a crucial part of hybrid video coding frameworks, utilized to exploit the temporal redundancy in video sequences and improve the coding performance. During inter prediction, a predicted block is typically derived from reference pictures using motion estimation and motion compensation. To improve the coding performance of inter prediction, a neural network based enhancement to inter prediction (NNIP) is proposed in this paper. NNIP is composed of three networks, namely residue estimation network, combination network, and deep refinement network. Specifically, first, a residue estimation network is designed to estimate the residue between current block and its predicted block using their available spatial neighbors. Second, the feature maps of the estimated residue and the predicted block are extracted and concatenated in a combination network. Finally, the concatenated feature maps are fed into a deep refinement network to generate a refined residue, which is added back to the predicted block to derive a more accurate predicted block. NNIP is integrated in HEVC to evaluate its efficiency. The experimental results demonstrate that NNIP can achieve 4.6%, 3.0%, and 2.7% BD-rate reduction on average under LDP, LDB, and RA configurations compared to HEVC. [ABSTRACT FROM AUTHOR]

As one of the fundamental technologies for scene understanding, semantic segmentation has been widely explored in the last few years. Light field cameras encode the geometric information by simultaneously recording the spatial information and angular information of light rays, which provides us with a new way to solve this issue. In this paper, we propose a high-quality and challenging urban scene dataset, containing 1074 samples composed of real-world and synthetic light field images as well as pixel-wise annotations for 14 semantic classes. To the best of our knowledge, it is the largest and the most diverse light field dataset for semantic segmentation. We further design two new semantic segmentation baselines tailored for light field and compare them with state-of-the-art RGB, video and RGB-D-based methods using the proposed dataset. The outperforming results of our baselines demonstrate the advantages of the geometric information in light field for this task. We also provide evaluations of super-resolution and depth estimation methods, showing that the proposed dataset presents new challenges and supports detailed comparisons among different methods. We expect this work inspires new research direction and stimulates scientific progress in related fields. The complete dataset is available at https://github.com/HAWKEYE-Group/UrbanLF. [ABSTRACT FROM AUTHOR]

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches. [ABSTRACT FROM AUTHOR]

Deep learning has greatly promoted the development of multi-view stereo in recent years. However, how to measure the reliability of the estimated depth map for practical applications and make reasonable depth hypothesis sampling for the cost volume building in the coarse-to-fine architecture are still unresolved crucial problems. To this end, an Uncertainty Guided multi-view Network (UGNet) is proposed in this paper. In order to enable the network to perceive the uncertainty, an uncertainty-aware loss function is introduced, which not only can infer uncertainty implicitly in an unsupervised manner but also can reduce the bad impact of high uncertainty regions and the erroneous labels in the training set during training. Moreover, an uncertainty-based depth hypothesis sampling strategy is further proposed to adaptively determine the depth search range of each pixel for finer stages, which helps to generate more rational depth intervals compared with other methods and build more compact cost volumes without redundancy. Experimental results on DTU dataset, BlendedMVS dataset, Tanks and Temples dataset and ETH3D high-res benchmark show that our method achieves promising reconstruction results compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, deep semi-supervised hashing methods have attracted increasing attention, which can significantly improve retrieval performance by leveraging abundant unlabeled data. These methods usually generate surrogate supervision signals to learn with unlabeled data, such as neighborhood information and augmentation invariant requirements. However, an essential issue of these methods is that the supervised signals are not always reliable, which may damage the performance. In this paper, we propose a novel Uncertainty-Aware and Multi-Granularity Consistent Constrained Semi-Supervised Hashing (UMCSH) method to alleviate the negative effects of noisy supervised signals and enlarge the inter-class distance. Specifically, our UMCSH mainly consists of an Uncertainty-Aware Instance-Level Consistency (UAILC) model and a Cluster-Based Class-Level Consistency (CBCLC) model. UAILC introduces an uncertainty estimation method to select reliable supervised signals to extract discriminative features for each unlabeled data. CBCLC establishes connections between labeled data and unlabeled data by encouraging each unlabeled sample to be close to the hash center (calculated with the labeled data) according to its pseudo-label. Extensive experimental results demonstrate the superior performance of our proposed approach compared with several state-of-the-art semi-supervised hashing methods. [ABSTRACT FROM AUTHOR]

Cross-modal hashing that leverages hash functions to project high-dimensional data from different modalities into the compact common hamming space, has shown immeasurable potential in cross-modal retrieval. To ease labor costs, unsupervised cross-modal hashing methods are proposed. However, existing unsupervised methods still suffer from two factors in the optimization of hash functions: 1) similarity guidance, they barely give a clear definition of whether is similar or not between data points, leading to the residual of the redundant information; 2) optimization strategy, they ignore the fact that the similarity learning abilities of different hash functions are different, which makes the hash function of one modality weaker than the hash function of the other modality. To alleviate such limitations, this paper proposes an unsupervised cross-modal hashing method to train hash functions with discriminative similarity guidance and adaptively-enhanced optimization strategy, termed Deep Adaptively-Enhanced Hashing (DAEH). Specifically, to estimate the similarity relations with discriminability, Information Mixed Similarity Estimation (IMSE) is designed by integrating information from distance distributions and the similarity ratio. Moreover, Adaptive Teacher Guided Enhancement (ATGE) optimization strategy is also designed, which employs information theory to discover the weaker hash function and utilizes an extra teacher network to enhance it. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed DAEH against the state-of-the-arts. [ABSTRACT FROM AUTHOR]

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance. [ABSTRACT FROM AUTHOR]

With the widespread use of deep learning in single object tracking task, mainstream tracking algorithms treat tracking as a combined classification and regression problem. Classification aims at locating an arbitrary target, and regression aims at estimating the corresponding bounding box. In this paper, we focus on regression and propose a novel box estimation network, which consists of a transformer encoder target pyramid guide (TPG) and transformer decoder target pyramid spread (TPS). Specifically, the transformer encoder TPG is designed to generate fine-grained prior knowledge with explicit representation for template targets. In contrast to the raw transformer encoder, we capture the visual dependence through local-global self-attention and deem the multi-scale target regions as the “local” region. Using this fine-grained prior knowledge, we design the transformer decoder TPS to spread it to the subsequent search regions with high affinity to accurately estimate the bounding boxes. Considering that self-attention fails to model information interaction across channels between the template target and search regions, we develop a channel-wise cross-attention block within the TPS as compensation. Extensive experiments on the OTB100, UAV123, NFS, VOT2020, VOT2021, LaSOT, LaSOT_ext, TrackingNet and GOT-10k benchmarks show that the proposed box estimation network outperforms most existing box estimation methods. Furthermore, our trackers based on this estimation network exhibit a competitive performance against state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Event cameras such as the Dynamic Vision Sensor (DVS) are useful because of their low latency, sparse output, and high dynamic range. In this paper, we propose a DVS+FPGA camera platform and use it to demonstrate the hardware implementation of event-based corner keypoint detection and adaptive block-matching optical flow. To adapt sample rate dynamically, events are accumulated in event slices using the area event count slice exposure method. The area event count is feedback controlled by the average optical flow matching distance. Corners are detected by streaks of accumulated events on event slice rings of radius 3 and 4 pixels. Corner detection takes about 6 clock cycles (16 MHz event rate at the 100MHz clock frequency) At the corners, flow vectors are computed in 100 clock cycles (1 MHz event rate). The multiscale block match size is $25\times 25$ pixels and the flow vectors span up to 30-pixel match distance. The FPGA processes the sum-of-absolute distance block matching at 123 GOp/s, the equivalent of 1230 Op/clock cycle. EDFLOW is several times more accurate on MVSEC drone and driving optical flow benchmarking sequences than the previous best DVS FPGA optical flow implementation, and achieves similar accuracy to the CNN-based EV-Flownet, although it burns about 100 times less power. The EDFLOW design and benchmarking videos are available at https://sites.google.com/view/edflow21/home. [ABSTRACT FROM AUTHOR]

Discriminative correlation filter (DCF)-based methods have demonstrated superior performance in UAV tracking via fusing multiple types of features and updating models online. However, most DCF-based trackers simply cascade different features, failing to fully take advantage of their complementary strength. In addition, online update strategies are limited to using a single and fixed learning rate, which often leads to model degradation when suffering tracking challenges. In this paper, we present an Auto-Perceiving Correlation Filter (APCF) which explicitly models the target and context with a novel Target State and Background Perception (TSBP) feature. Concretely, we first propose a simple yet effective State Evaluation Metric (SEM) to estimate target states by analyzing the spatial distribution of responses. Based on SEM, we extract TSBP features by adaptively selecting effective features depending on the current target state. Accordingly, a new online model update strategy is also introduced to avoid model degradation. Moreover, we further introduce a perception regularization term to make the extracted feature emphasis more on the target rather than background. Extensive experiments on four widely-used UAV benchmarks have well demonstrated the superiority of the proposed method compared with both DCF and deep learning based trackers while running at a high speed of 76.7 FPS on a single CPU. In addition, APCF with deep features also performs favorably against state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Given a face image, most of previous works in gaze estimation infer the gaze via a well-trained model with supervised training. However, the distribution of test data may be very different compared to that of training data since samples might be corrupted in real-world scenarios (e.g., taking a photo in strong light). This will lead to a gap between source domain (i.e., training data) and target domain (i.e., test data). In this paper, we first introduce self-supervised learning into our method for addressing challenging situations in gaze estimation. Moreover, existing appearance-based gaze estimation methods focus on directing towards the development of powerful regressors, which mainly utilize face and eye images simultaneously or face (eye) images only. However, the problem of inter cues between face and eye features has been largely overlooked. To this end, we propose a novel Modulation-based Adaptive Network (MANet) for gaze estimation, which uses high-level knowledge to filter the distractive information and bridges the intrinsic relationship between face and eye features. Further, we combine self-supervised learning and MANet to learn to adapt to challenging cases, such as abnormal lighting conditions and poor-quality images, by minimizing a self-supervised loss and a supervised loss jointly. The experimental results on several datasets demonstrate the effectiveness of our proposed approach with a real-time speed of 900 fps on a PC with an NVIDIA Titan RTX GPU. [ABSTRACT FROM AUTHOR]

Monocular depth estimation, as one of the fundamental tasks of computer vision, plays a crucial role in three-dimensional (3D) scene understanding and perception. Usually, deep learning methods recover monocular depth maps using continuous regression manners by minimizing the errors between the ground-truth depth and the predicted depth. However, fine depth features may not be fully captured through layer-by-layer coding, which is prone to low spatial resolution depth maps and insufficient details. Furthermore, it usually converges slowly and suffers from unsatisfactory results. To tackle these issues, we propose a novel model, named context-based ordinal regression network (CORNet), to reconstruct monocular depth maps in the ordinal regression manner with context information in this paper. Firstly, we put forward a novel context-based encoder with a feature transformation (FT) module to learn context information and details from inputs, and output multi-scale feature maps. Then, we design a boundary enhancement module (BEM) with a spatial attention mechanism following each operation of feature fusion, which captures boundary features in the scene to enhance the border depth. Finally, a feature optimization module (FOM) is designed to fuse and optimize the multi-scale features and boundary features to strengthen depth learning. What’s more, we introduce an ordinal weighted inference to predict depth maps from probabilities and discretization values. Experiments and results on two challenging datasets, KITTI and NYU Depth V2, demonstrate that our proposed CORNet can estimate monocular depth maps effectively and obtain superior performance in capturing geometric features over existing methods. [ABSTRACT FROM AUTHOR]

Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration. Traditional homography estimation methods heavily depend on the quantity and distribution of feature correspondences, leading to poor robustness in low-texture scenes. The learning solutions, on the contrary, try to learn robust deep features but demonstrate unsatisfying performance in the scenes with low overlap rates. In this paper, we address these two problems simultaneously by designing a contextual correlation layer (CCL). The CCL can efficiently capture the long-range correlation within feature maps and can be flexibly used in a learning framework. In addition, considering that a single homography can not represent the complex spatial transformation in depth-varying images with parallax, we propose to predict multi-grid homography from global to local. Moreover, we equip our network with a depth perception capability, by introducing a novel depth-aware shape-preserved loss. Extensive experiments demonstrate the superiority of our method over state-of-the-art solutions in the synthetic benchmark dataset and real-world dataset. The codes and models will be available at https://github.com/nie-lang/Multi-Grid-Deep-Homography. [ABSTRACT FROM AUTHOR]

In image forensics, quantization step estimation plays a crucial role in revealing the JPEG compression history. This study focuses on the estimation for images that have been JPEG compressed and re-saved in lossless formats. Several effective methods have been developed, whereas the performance on small sized images still needs to be improved. In this paper, a novel JPEG quantization step estimation method with low complexity and high efficiency is proposed. First, for a given decompressed image, as the special shape of its DCT coefficients distribution is highly related to the quantization step, a function of the candidate step taking a similar shape is designed. Then, the quantization step is determined as the candidate leading to the maximum response of the designed function on the probability density function of DCT coefficients. The relation between the maximum response and the quantization step is verified mathematically, which provides a theoretical basis for the proposed method. In addition, to further improve the estimation performance, two fine adjustment procedures are adopted. Experimental results demonstrate that the proposed method outperforms some state-of-the-art works, especially on small sized images. [ABSTRACT FROM AUTHOR]

In this paper, we propose an iterative feature matching framework for self-supervised depth estimation in indoor scenes. Conventional methods usually leverage the structure-from-motion supervision to help the photometric optimization escape from the local minima, which have complex ego-motion and large regions with non-texture or repeated-texture. However, the supervision is limited as the reconstruction is usually sparse. To address this, we propose an iterative feature matching framework called IFMNet to jointly learn depths and search for correspondences. With the predicted depths from the previous iteration, we present an online optimized grid searching algorithm to find more accurate correspondences. Given these new correspondences, we compute the triangulated depths and improve the depth network with adaptive bin-wise online hard example mining. Experimental results on the NYU Depth V2 and SceneNet datasets verify the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

A noise-suppressing low-light image enhancement approach is proposed in this paper based on the extent of exposedness at each image pixel. To this end, a progressive, structure-aware exposedness estimation procedure is presented that quantifies local and global exposedness. These exposedness values are leveraged to produce a locally smooth pixel-level map that signifies the required degrees of enhancement at image pixels. This map is subsequently used in an enhancement function, which satisfies a few important properties, to generate the enhanced image. Before the enhancement, inherent noise in the low-light image is diminished employing a detail-preserving, low gradient magnitude suppression method. Subjective and quantitative analysis of results on a wide variety of natural and synthetically generated low-light images from standard databases using PSNR, iRSE, SSIM, and measures of perceptual quality, natural image statistics and brightness preservation suggests that our approach in general outperforms the state-of-the-art. Ablation studies and further experiments show the importance of a few components of our approach, and that our approach is computationally fast. [ABSTRACT FROM AUTHOR]

In reversible data hiding (RDH) schemes, how to select those smooth pixels, pixel pairs or pixel blocks in order to improve performance is an important issue. For pixel-value-ordering (PVO) based RDH schemes, two existing techniques appear to be defective since only two reference pixels in a block or the right and bottom neighbors of a block are exploited as the context for block selection, and their performance might be only adequate for those smooth images. For rough images, the embedding performance could be affected. In this paper, an efficient block selection method by computing a block’s smoothness with a full-enclosing context (FEC) way is proposed. Obtained results show that the proposed FEC strategy can better estimate a block’s smoothness for PVO-based schemes. Furthermore, a more scalable pairing way is presented for the recently reported location-based PVO predictor. The proposed PVO scheme can be implemented by dividing the cover image and embedding bits into two different types of blocks, respectively. Experimental results show that the marked images by the proposed two-stage PVO scheme have higher visual quality, e.g., the average PSNR for the Kodak image database is 63.31 dB after embedding 10,000 bits, and the gain is 0.16 dB against the best result in the literature. Compared with some state-of-the-art RDH works, the superiority of the proposed algorithm has been verified in extensive experiments. [ABSTRACT FROM AUTHOR]

Efficient real-time disparity estimation is critical for the application of stereo vision systems in various areas. Recently, stereo network based on coarse-to-fine method has largely relieved the memory constraints and speed limitations of large-scale network models. Nevertheless, all of the previous coarse-to-fine designs employ constant offsets and three or more stages to progressively refine the coarse disparity map, still resulting in unsatisfactory computation accuracy and inference time when deployed on mobile devices. This paper claims that the coarse matching errors can be corrected efficiently with fewer stages as long as more accurate disparity candidates can be provided. Therefore, we propose a dynamic offset prediction module to meet different correction requirements of diverse objects and design an efficient two-stage framework. In addition, a disparity-independent convolution is proposed to regularize the compact cost volume efficiently and further improve the overall performance. The disparity quality and efficiency of various stereo networks are evaluated on multiple datasets and platforms. Evaluation results demonstrate that, the disparity error rate of the proposed network achieves 2.66% and 2.71% on KITTI 2012 and 2015 test sets respectively, where the computation speed is $2\times $ faster than the state-of-the-art lightweight models on high-end and source-constrained GPUs. [ABSTRACT FROM AUTHOR]

Depth estimation from a camera is an important task for 3D perception. Recently, without using the labeled ground truth of depth map, a self-supervised deep learning network can use relative pose to synthesize the target image from the reference image, and the photometric error between synthesized reference image and real one is used as self-supervisory signal. In this paper, we propose a novel self-supervised depth estimation network, which takes advantage of the quadtree constraint to optimize the depth estimation network. Based on the quadtree constraint, the photometric loss and depth loss of quadtree are proposed. In order to solve the problem that multiple depth values in repeated structures and uniform texture regions can cause relatively low photometric loss, we use quadtree-based photometric loss, which calculates the averaged photometric loss in quadtree blocks instead of the pixel-wise loss. For the problem of imbalanced depth distribution, we use quadtree depth loss, which constrains the depth inconsistency within quadtree blocks. The depth estimation network is composed of deep fusion module and cross-layer feature fusion module, which can better extract the feature information of RGB image and sparse keypoints depths, and makes full use of the detail information of the shallow feature map and the semantic information of the deep feature map to enrich the feature information extraction. Experimental results demonstrate that our method outperforms the state-of-the-art approaches of depth estimation. [ABSTRACT FROM AUTHOR]

Depth estimation is crucial in several computer vision applications, and a recent trend in this field aims at inferring such a cue from a single camera. Unfortunately, despite the compelling results achieved, state-of-the-art monocular depth estimation methods are computationally demanding, thus precluding their practical deployment in several application contexts characterized by low-power constraints. Therefore, in this paper, we propose a lightweight Convolutional Neural Network based on a shallow pyramidal architecture, referred to as $\mu $ PyD-Net, enabling monocular depth estimation on microcontrollers. The network is trained in a peculiar self-supervised manner leveraging proxy labels obtained through a traditional stereo algorithm. Moreover, we propose optimization strategies aimed at performing computations with quantized 8-bit data and map the high-level description of the network to low-level layers optimized for the target microcontroller architecture. Exhaustive experimental results on standard datasets and an in-depth evaluation with a device belonging to the popular Arm Cortex-M family confirm that obtaining sufficiently accurate monocular depth estimation on microcontrollers is feasible. To the best of our knowledge, our proposal is the first one enabling such remarkable achievement, paving the way for the deployment of monocular depth cues onto the tiny end-nodes of distributed sensor networks. [ABSTRACT FROM AUTHOR]

Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming. However, higher-order relationships across the entire camera network are ignored by these methods, leading to contradictory outputs when matching results from different camera pairs are combined. In this paper, we address the problem of unsupervised video-based re-identification by proposing a consistent cross-view matching (CCM) framework, in which global camera network constraints are exploited to guarantee the matched pairs are with consistency. Specifically, we first propose to utilize the first neighbor of each sample to discover relations among samples and find the groups in each camera. Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network. Finally, we learn metric models for camera pairs progressively by alternatively mining consistent cross-view matching pairs and updating metric models using these obtained matches. Rigorous experiments on two widely-used benchmarks for video re-identification demonstrate the superiority of the proposed method over current state-of-the-art unsupervised methods; for example, on the MARS dataset, our method achieves an improvement of 4.2% over unsupervised methods, and even 2.5% over one-shot supervision-based methods for rank-1 accuracy. [ABSTRACT FROM AUTHOR]

Haze caused by atmospheric scattering and absorption would severely affect scene visibility of an image. Thus, image dehazing for haze removal has been widely studied in the literature. Within a hazy image, haze is not confined in a small local patch/position, while widely diffusing in a whole image. Under this circumstance, global context is a crucial factor in the success of dehazing, which was seldom investigated in existing dehazing algorithms. In the literature, the global context (GC) block has been designed to learn point-wise long-range dependencies of an image for global context modeling; however, patch-wise long-range dependencies were ignored. To image dehazing, patch-wise long-range dependencies should be highlighted to cooperate with patch-wise operations of image dehazing. In this paper, we first extend the point-wise GC into a Pyramid Global Context (PGC), which is a multi-scale GC, after undergoing the pyramid pooling. Thus, patch-wise long-range dependencies can be explored by the PGC. Then, the proposed PGC is plugged into a U-Net, getting an attentive U-Net. Further, the attentive U-Net is optimized by importing ResNet’s shortcut connection and dilated convolution. Thus, the finalized dehazing model can explore both long-range and patch-wise context dependencies for global context modeling, which is crucial for image dehazing. The extensive experiments on synthetic databases and real-world hazy images demonstrate the superiority of our model over other representative state-of-the-art models from both quantitative and qualitative comparisons. [ABSTRACT FROM AUTHOR]

The success of convolutional neural network (CNN) based single image dynamic scene blind deblurring (SIDSBD) methods mainly stems from the multi-scale/multi-patch model and the designs of the encoder-decoder architecture, and the residual block structure, which make different contributions to SIDSBD. In this paper, we further exploit the advantages of the multi-scale model, the encoder-decoder module, and the residual block structure, respectively, and propose a novel multi-scale channel attention network (MSCAN) for effective single image dynamic scene blind deblurring. Different from existing multi-scale models, in our proposed network, each scale consists of multiple levels, in which a novel spatial pyramid pooling channel attention (SPPCA) strategy is proposed to adaptively rescale the channel-wise features by using both the global and local feature statistics for more powerful network representation. Extensive experiments on both the synthetic benchmark datasets and the real blurred images show that our method can produce better deblurring results than the state-of-the-art SIDSBD methods in terms of both qualitative evaluation and quantitative metrics. [ABSTRACT FROM AUTHOR]

Disparity estimation is an essential task taking part in many light-field applications. Due to the complexity of algorithms and high dimensional property of light-field data, performing this task involves a significant computational effort and results in very long processing time on CPU. Graphics processing units (GPUs), which is capable of massively parallel processing, is a promising solution to cover the computation requirement and speed up the task. In this paper, we develop a GPU-accelerated approach for light-field disparity estimation using a variational computation framework (GVLD). Our algorithm combines the intrinsic sub-pixel precision of variational formulation and the effectiveness of weighted median filtering to produce a highly accurate solution. The proposed algorithm is fully parallelized and optimized for the implementation using the OpenCL framework. An intensive evaluation including a quantitative comparison to related works and a detailed analysis of the proposed approach’s performance is presented. Experimental results demonstrate our superior performance compared to state-of-the-art approaches. The proposed approach is 10+ times faster than other approaches running on a similar GPU platform and provides the most accurate solution among optimization-based approaches. Compared to the implementation running on CPU, our GPU-accelerated method achieves up to $365\times $ speed up. [ABSTRACT FROM AUTHOR]

The latest High Efficiency Video Coding standard achieves twice the coding efficiency of the H264 standard through a complex rate-distortion optimization (RDO). The coded bit-streams are produced with context adaptive binary arithmetic coding (CABAC). CABAC itself is a very time-consuming process that includes binarization, context modeling, interval subdivision, renormalization, outstanding bit handling, and context updating. The aim of this research is to speed up the CABAC process through several simplifications. First, we approximate three parts of the CABAC, i.e., interval subdivision, renormalization, and outstanding bit handling, with a piecewise-linear function that is very friendly to hardware implementation. In order to achieve better hardware parallelism, we also improve the coding process at the sub-block level. The context of syntax elements in a sub-block is redistributed to skip the complex calculation of context indexing. We perform context updating at the granularity of sub-blocks so that the data dependency of the context updating is removed completely, and the original serial encoding process is changed to a parallel encoding process. At the same time, we make another simplification for the context modeling of cu_skip_flag. Based on these simplifications, we build a parallel hardware architecture for the rate estimation of the RDO process. This architecture completes the bit estimation of a $32\times 32$ coding tree unit (CTU) in 220.8 nano-seconds, whereas the Bjøntegaard Delta rate increases by only 2.225%. We believe that the proposed architecture can meet the requirements of 8K@120 fps ultra-high-definition videos. This is the first study to simplify the hardware design of rate estimation by changing the context allocation and updating rules. [ABSTRACT FROM AUTHOR]

Bio-inspired event cameras have been considered effective alternatives to traditional frame-based cameras for stereo depth estimation, especially in challenging conditions such as low-light or high-speed environments. Recently, deep learning-based supervised event stereo matching methods have achieved significant performance improvements over the traditional event stereo methods. However, the supervised methods depend on ground-truth disparity maps for training, and it is difficult to secure a large amount of ground-truth disparity maps. A feasible alternative is to devise an unsupervised event stereo method that can be trained without ground-truth disparity maps. To this end, we propose the first unsupervised event stereo matching method that can predict dense disparity maps, and is trained by transforming the depth estimation problem into a warping-based reconstruction problem. We propose a novel unsupervised loss function that enforces the network to minimize the feature-level epipolar correlation difference between the ground-truth intensity images and warped images. Moreover, we propose a novel event embedding mechanism that utilizes both temporal and spatial neighboring events to capture spatio-temporal relationships among the events for stereo matching. Experimental results reveal that the proposed method outperforms the baseline unsupervised methods by significant margins (e.g., up to 16.88% improvement) and achieves comparable results with the existing supervised methods. Extensive ablation studies validate the efficacy of the proposed modules and architectural choices. [ABSTRACT FROM AUTHOR]

To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations. PSCC-Net processes the image in a two-path procedure: a top-down path that extracts local and global features and a bottom-up path that detects whether the input image is manipulated, and estimates its manipulation masks at multiple scales, where each mask is conditioned on the previous one. Different from the conventional encoder-decoder and no-pooling structures, PSCC-Net leverages features at different scales with dense cross-connections to produce manipulation masks in a coarse-to-fine fashion. Moreover, a Spatio-Channel Correlation Module (SCCM) captures both spatial and channel-wise correlations in the bottom-up path, which endows features with holistic cues, enabling the network to cope with a wide range of manipulation attacks. Thanks to the light-weight backbone and progressive mechanism, PSCC-Net can process $1,080\text{P}$ images at 50+FPS. Extensive experiments demonstrate the superiority of PSCC-Net over the state-of-the-art methods on both detection and localization. Codes and models are available at https://github.com/proteus1991/PSCC-Net. [ABSTRACT FROM AUTHOR]

Human densepose estimation, aiming at establishing dense correspondences between 2D pixels of human body and 3D human body template, is a key technique in enabling machines to have an understanding of people in images. It still poses several challenges due to practical scenarios where real-world scenes are complex and only partial annotations are available, leading to incompelete or false estimations. In this work, we present a novel framework to detect the densepose of multiple people in an image. The proposed method, which we refer to Knowledge Transfer Network (KTN), tackles two main problems: 1) how to refine image representation for alleviating incomplete estimations, and 2) how to reduce false estimation caused by the low-quality training labels (i.e., limited annotations and class-imbalance labels). Unlike existing works directly propagating the pyramidal features of regions for densepose estimation, the KTN uses a refinement of pyramidal representation, where it simultaneously maintains feature resolution and suppresses background pixels, and this strategy results in a substantial increase in accuracy. Moreover, the KTN enhances the ability of 3D based body parsing with external knowledges, where it casts 2D based body parsers trained from sufficient annotations as a 3D based body parser through a structural body knowledge graph. In this way, it significantly reduces the adverse effects caused by the low-quality annotations. The effectiveness of KTN is demonstrated by its superior performance to the state-of-the-art methods on DensePose-COCO dataset. Extensive ablation studies and experimental results on representative tasks (e.g., human body segmentation, human part segmentation and keypoints detection) and two popular densepose estimation pipelines (i.e., RCNN and fully-convolutional frameworks), further indicate the generalizability of the proposed method. [ABSTRACT FROM AUTHOR]

Existing face hallucination methods always achieve improved performance through regularizing the model with facial prior. Most of them always estimate facial prior information first and then leverage it to help the prediction of the target high-resolution face image. However, the accuracy of prior estimation is difficult to guarantee, especially for the low-resolution face image. Once the estimated prior is inaccurate or wrong, the following face super-resolution performance is unavoidably influenced. A natural question that arises: how to incorporate facial prior effectively and efficiently without prior estimation? To achieve this goal, we propose to learn facial prior knowledge at training stage, but test only with low-resolution face image, which can overcome the difficulty of estimating accurate prior. In addition, instead of estimating facial prior, we directly explore the potential of high-quality facial prior in the training phase and progressively propagate the facial prior knowledge from the teacher network (trained with the low-resolution face/high-quality facial prior and high-resolution face image pairs) to the student network (trained with the low-resolution face and high-resolution face image pairs). Quantitative and qualitative comparisons on benchmark face datasets demonstrate that our method outperforms the state-of-the-art face super-resolution methods. The source codes of the proposed method will be available at https://github.com/wcy-cs/KDFSRNet. [ABSTRACT FROM AUTHOR]

Optical flow estimation is a fundamental task in computer vision and image processing. Due to the difficulty in obtaining the ground truth of flow field, unsupervised learning approaches attract more and more research interests in recent years. However, despite of their good generalization capability, unsupervised optical flow methods suffer in the scenarios with large displacement, small objects, and occlusions. In this work, we propose a novel optical flow network based on decoder with multi-scale kernels. Different from previous U-Net like or pyramidal methods, we design our network based on RAFT architecture that with a 4D correlation layer and recurrent decoder. More importantly, we incorporate three novel ideas with regard to the input, information processing and output of the update units improve the performance. Firstly, we utilize various motion-related information as input to the update units. Secondly, we propose a module of multi-scale update unit. Thirdly, for the final flow up-sampling procedure, we propose an image-guided up-sampling loss to guide the learning of up-sampling masks. Our model is trained by the occlusion-aware photometric loss, edge-aware smoothness loss, self-supervised loss, and image-guided up-sampling loss. Experimental results demonstrate that our model achieves the state-of-the-art performance on both Sintel and KITTI and outperforms other unsupervised optical flow methods remarkably. [ABSTRACT FROM AUTHOR]

Existing stereo matching methods achieve satisfactory average accuracy on a whole predicted disparity map under common global metrics, but ignore the fine-grained performance at region level, especially for far regions in the scene, which is more crucial in actual auto-driving scenarios. There are two factors accounting for this problem: 1) Depth resolution. Existing methods use disparity-based sampling to extract matching candidates uniformly according to the disparity range, but leads to sparser sampling density at far regions than that of close regions in terms of depth range, resulting in low depth resolution in far regions. 2) Feature discriminability. Limited image resolution and inferior feature extraction at far regions result in the obtained features with low discrimination, which influences the subsequent matching process between stereo images. To improve the estimation accuracy of far regions and thus achieve a balanced performance at region level, we design a novel two-stage Balanced Stereo Matching Network (BSMNet) to address the above problems. The coarse stage of BSMNet introduces a direct depth-based sampling strategy, which generates matching candidates according to scene depth instead of disparity, thus improving the depth resolution and obtaining initial depth map with more balanced accuracy. Then, a depth refinement stage is proposed to solve the problem of low feature discriminability and further optimizes the initial depth map obtained from the coarse stage. It selects matching candidates and computes their similarity scores from a carefully designed adaptive feature volume guided by a learnable scale map, thus making the final estimation more accurate. Different from the existing methods that construct stereo matching based on disparity prediction, our proposed pipeline is to directly optimize on depth information. Experiments show that our BSMNet can obtain an obvious performance improvement at far regions without discarding that at close regions, so as to largely outperform existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This letter introduces an underwater image enhancement method to handle low contrast and color cast of underwater images. Firstly, with the help of hierarchical searching technique, we propose a novel backscattered light estimation method. And in this procedure, a novel scoring formula is considered into our method, which comprehensively considers multiple prior knowledge. Then, we generalize underwater dark channel prior (UDCP) approach to obtain more robust transmission estimation. In addition, we also develop a white balance method to further modify the appearance of the resultant image. Extensive experiments on real-world images demonstrate that the proposed method outperforms several previous image restoration or enhancement works. [ABSTRACT FROM AUTHOR]

Underwater captured images are usually degraded by low contrast, hazy, and blurry due to absorbing and scattering, which limits their analyses and applications. To address these problems, a red channel prior guided variational framework is proposed based on the complete underwater image formation model (UIFM). Unlike most of the existing methods that only consider the direct transmission and backscattering components, we additionally include forward scattering component into the UIFM. In the proposed variational framework, we successfully incorporate the normalized total variation item and sparse prior knowledge of blur kernel together. In addition, we perform the estimation of blur kernel by varying image resolution in a coarse-to-fine manner to avoid local minima. Moreover, for solving the generated non-smooth optimization problem, we employ the alternating direction method of multipliers (ADMM) to accelerate the whole progress. Experimental results demonstrate that the proposed method has a good performance on dehazing and deblurring. Extensive qualitative and quantitative comparisons further validate its superiority against the other state-of-the-art algorithms. The code is available online at: https://github.com/Hou-Guojia/UNTV [ABSTRACT FROM AUTHOR]

Interpolating video frames involving large motions remains an elusive challenge. In case that frames involve small and fast-moving objects, conventional feed-forward neural network-based approaches that estimate optical flow and synthesize in-between frames sequentially often result in loss of motion features and thus blurred boundaries. To address the problem, we propose a novel Recurrent Motion-Enhanced Interpolation Network (ReMEI-Net) by assigning attention to the motion features of small objects from both the intra-scale and inter-scale perspectives. Specifically, we add recurrent feedback blocks in the existing multi-scale autoencoder pipeline, aiming to iteratively enhance the motion information of small objects across different scales. Second, to further refine the motion features of the highly moving objects, we propose a Multi-Directional ConvLSTM (MD-ConvLSTM) block to capture the global spatial contextual information of motion from multiple directions. In this way, the coarse-scale features can be utilized to correct and enhance the fine-scale features through the feedback mechanism. Extensive experiments on various datasets demonstrate the superiority of our proposed method over state-of-the-art approaches in terms of clear locations and complete shape. [ABSTRACT FROM AUTHOR]

Correlation filters (CF), a popular tool for visual tracking, suffer from unwanted boundary effects due to the periodic assumption needed for FFT implementation. To address this issue, spatially regularized discriminative correlation filters (SRDCF) have been proposed by introducing a weighting matrix to the regularization term. However, the existing design of spatial weighting matrix is often heuristic and non-adaptive. Inspired by recent advances in joint discrimination and reliability learning for correlation tracking, we propose a principled Bayesian correlation filter learning method using Gaussian scale mixture (GSM) model. The key idea is to decompose each CF coefficient into the product of a positive scalar multiplier and a Gaussian random variable. Treating positive multipliers as weighting coefficients, GSM-based modeling of CFs leads to a spatially adaptive regularization strategy with improved capability of handling various appearance-related uncertainty factors (e.g., scale variation, out-of-plane rotation, and motion blur). Moreover, by imposing a sparse prior over the multipliers, we can jointly learn multipliers and CFs under a unified Bayesian estimation framework. Structured GSM model allows us to better exploit the spatial correlations among CFs and further improve the tracking performance. Experimental results on OTB-2013, OTB-2015, Temple Color-128, VOT-2016, and VOT-2017 show that our tracking method performs favorably when compared with current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Immersive video often refers to multiple views with texture and scene geometry information, from which different viewports can be synthesized on the client side. To design efficient immersive video coding solutions, it is desirable to minimize bitrate, pixel rate and complexity. We investigate whether the classical approach of sending the geometry of a scene as depth maps is appropriate to serve this purpose. Previous work shows that bypassing depth transmission entirely and estimating depth at the client side improves the synthesis performance while saving bitrate and pixel rate. In order to understand if the encoder side depth maps contain information that is beneficial to be transmitted, we first explore a hybrid approach which enables partial depth map transmission using a block-based RD-based decision in the depth coding process. This approach reveals that partial depth map transmission may improve the rendering performance but does not present a good compromise in terms of compression efficiency. This led us to address the remaining drawbacks of decoder side depth estimation: complexity and depth map inaccuracy. We propose a novel system that takes advantage of high quality depth maps at the server side by encoding them into lightweight features that support the depth estimator at the client side. These features allow reducing the amount of data that has to be handled during decoder side depth estimation by 88%, which significantly speeds up the cost computation and the energy minimization of the depth estimator. Furthermore, −46.0% and −37.9% average synthesis BD-Rate gains are achieved compared to the classical approach with depth maps estimated at the encoder. [ABSTRACT FROM AUTHOR]

We present an effective blind image deblurring algorithm based on superpixel segmentation prior (SSP). The motivation of this work is an interesting observation that the more rough the segmentation boundaries are, the clearer the image will be. Intuitively, the blurry images have less image details, which results in more smooth segmentation boundaries. The clear images have more vivid textural details and obtain more rough boundaries. The segmentation roughness could be defined as the length of image segmentation boundaries. However, the segmentation boundary length is not differentiable, making it difficult to integrate into existing joint optimization framework. Therefore, we transform the segmentation boundary length into the segmentation entropy to guide the process of image deblurring. With the image becomes clearer, its boundary becomes more rough, while the segmentation entropy is much smaller. The analysis of relationship between segmentation entropy and segmentation boundaries is detailed. Benefiting from the convexity of segmentation entropy, we propose a novel algorithm by integrating half-quadratic split and gradient descent to alternately minimize energy function. Extensive experiments show that the proposed method achieves best performance with the state-of-the-art blind deblurring methods on natural and face image deblurring. [ABSTRACT FROM AUTHOR]

How to automatically predict people’s gaze has attracted attention in the field of computer vision and machine learning. Previous studies on this topic set many constraints, such as restricted scenarios and strict and complex inputs. To mitigate these constraints to predict the gaze of people in more general scenarios, we propose a three-pathway network (TPNet) to estimate gaze via the joint modeling of multiple cues. Specifically, we first design a human-centric relationship inference (HCRI) module to learn the object-level relationship between the target person and the surrounding persons/objects in a scene. To the best of our knowledge, this is the first time that the object-level relationship is introduced into the gaze estimation task. Then, we construct a novel deep network with three pathways to fuse multiple cues, including scene saliency, object-level relationships and head information, to predict the gaze target. In addition, to extract the multilevel features during network training, we build and embed a micropyramid module in TPNet. The performance of TPNet is evaluated on two gaze estimation datasets: GazeFollow and DLGaze. A large number of quantitative and qualitative experimental results verify that TPNet can obtain robust results and significantly outperform the existing state-of-the-art gaze estimation methods. The code of TPNet will be released later. [ABSTRACT FROM AUTHOR]

JPEG-domain enhancement improves the visual quality of JPEG images by directly manipulating the decoded DCT (discrete cosine transform) coefficients, which inevitably leads to mixed compression and enhancement artifacts. Existing forensic methods that merely consider JPEG artifacts are unsuitable to address such mixed artifacts and hence suffer a considerable performance decline in compression parameter estimation and lack the ability to estimate the enhancement parameter. This work attempts to explore the characterization of the mixed artifacts, and to further estimate both the enhancement and compression parameters of JPEG-domain enhanced images. First, a statistical likelihood function is proposed to characterize the periodicity of DCT coefficients, which can measure how well an enhanced image is de-enhanced back to its JPEG compressed version given the compression and enhancement parameters. The proposed likelihood function reaches its maximum if the parameters match their true values. Then, a forensic method of enhancement detection and parameter estimation is developed based on the proposed likelihood function for two kinds of classical JPEG-domain enhancement. Specifically, JPEG-domain enhanced images are detected by thresholding a scalar feature computed upon the likelihoods, and the enhancement and compression parameters are estimated by locating the maximal likelihood. In addition, mathematical proof of the de-enhancement feasibility is provided. Experimental results demonstrate that the proposed method outperforms the compared methods in both enhancement detection and parameter estimation. [ABSTRACT FROM AUTHOR]

View-synthesis-based methods have shown very promising results for the task of unsupervised depth estimation in single images. Most existing approaches synthesize a new image and employ it as the supervision signal for depth and pose prediction. There are two problems in these approaches: 1) There are many combinations of pose and depth that can synthesize a certain new image; therefore, reconstructing the depth and pose based on the view-synthesis method from only two images is an inherently ill-posed problem; 2) The model is trained under the photometric consistency assumption that the brightness or gradient is constant when applied to the video sequences. However, this assumption is easily violated in realistic scenes due to light changes, reflective surfaces and occlusions. To overcome the first drawback, we exploit the point cloud consistency constraint to eliminate ambiguity. To overcome the second drawback, we use threshold masks to filter dynamic and occluded points and introduce matching point constraints that implicitly encode the geometry relationship between two matched points to improve the precision of depth prediction. In addition, we employ epipolar constraints to compensate for the instability of the photometric error in textureless regions and varying illumination conditions. The experimental results on the KITTI, Cityscapes and NYUv2 datasets show that the method can improve the accuracy of depth prediction and enhance the robustness of the model in handling textureless regions and illumination changes. The code and data are available at https://github.com/XTUPRLAB/FixUnDepth. [ABSTRACT FROM AUTHOR]

Raining is a common weather condition, and may seriously degrade the performances of outdoor computer vision systems, such as surveillance and autonomous navigation. Rain streaks may exhibit diverse appearances in the captured images, depending on their distances from the camera. For example, sparse rain streaks near the camera lens may appear as continuous and translucent strips, while distant densely accumulated rain streaks are more like fog and mist. Existing rain removal methods are mainly based on a single input image. However, on a single image, it is difficult to estimate a reliable depth map for rain removal. A light field image (LFI) records abundant structural and texture information of the target scene by capturing multi-perspective sub-aperture views with a single exposure. With a LFI, it is easier to estimate the depth maps, and rain streak locations across sub-aperture views are highly correlated. We observe that rain streaks usually have different slops and/or chromaic values, compared with the background scene, along the epipolar plane images (EPIs) of an LFI. Thus, we propose to make use of 3D EPIs to detect rain streaks and restore the background. To this end, we propose a novel GAN architecture to remove rain streaks from an LFI. Our method takes as input a 3D EPI, i.e., a stacked of sub-aperture views along the same row of a rainy LFI. It first estimates the disparity maps for the 3D EPI by utilizing an auto-encoder based depth estimation sub-network. The disparity maps concatenated with the input sub-aperture views are then fed into a non-local residual block, and two branched autoencoder sub-networks are used to extract rain-streaks and recover rain-free sub-aperture views. Extensive experiments conducted on both synthetic real-world-like LFIs and real-world LFIs demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

With a great success of the generative model via deep neural networks, monocular depth estimation has been actively studied by exploiting various encoder-decoder architectures. However, the decoding process in most previous methods, which repeats simple up-sampling operations, probably fails to fully utilize underlying properties of well-encoded features for monocular depth estimation. To resolve this problem, we propose a simple but effective scheme by incorporating the Laplacian pyramid into the decoder architecture. Specifically, encoded features are fed into different streams for decoding depth residuals, which are defined by decomposition of the Laplacian pyramid, and corresponding outputs are progressively combined to reconstruct the final depth map from coarse to fine scales. This is fairly desirable to precisely estimate the depth boundary as well as the global layout. We also propose to apply weight standardization to pre-activation convolution blocks of the decoder architecture, which gives a great help to improve the flow of gradients and thus makes optimization easier. Experimental results on benchmark datasets constructed under various indoor and outdoor environments demonstrate that the proposed method is effective for monocular depth estimation compared to state-of-the-art models. The code and model are publicly available at: | https://github.com/tjqansthd/LapDepth-release |. [ABSTRACT FROM AUTHOR]

An image dehazing technique can restore hazy images to clear ones. Under hazy conditions, it can be a preceding stage of advanced-driver assistance systems (ADAS) to keep driving images clear. ADAS pursues a larger image resolution with a higher frame rate to keep its reliability for driving safety. This trend drives the image dehazing to face a severe challenge of considerable throughput. In this study, a hardware-efficient image dehazing engine with low computational burdens is proposed. It comprises two core techniques: Depth-Refinement Transmission-Rate Estimation (DTE) and Distribute Airlight Estimation (DAE). DTE provides an accurate depth map to estimate transmission rate, where the depth of white-objects is carefully calibrated. Base on the depth information, DAE predicts the airlight with the average of distant illumination sources to compensate nonuniform haze concentration. DTE and DAE can reduce the number of line buffers by 33% at least, while keeping comparable image dehazing quality. This study is implemented using TSMC CMOS $0.18~\mu \text{m}$ technology. To construct a smooth data scheduling, it is organized into a six-stage pipelinging strategy with an interleaving access technique. The logic gate count is 14.4K, and the power consumption is 15.2mW@250MHz. With a two-level parallelism, its throughput can support 3, $840\times 2.160$ @60fps. The experiment results show this study presents the superior combined performance of energy efficiency, area efficiency and line buffer reduction. [ABSTRACT FROM AUTHOR]