Search

In this paper, a novel Extended Logistic Chaotic Map (ELCM) with two control parameters is proposed. Overcoming the major drawback of the standard Logistic and other existing chaotic maps, the ELCM not only has infinite chaotic range as well as good ergodicity, but also has a simple structure just like the Logistic map, which greatly facilitates its practical implementation and becomes very suitable for today's real-time applications. Moreover, a new color image encryption scheme based on chaos concept, DNA (Deoxyribonucleic Acid) encoding and Convolutional Neural Network (CNN) is also presented. To perform a chaotic scrambling, the ELCM is however used at different stages of the encryption process. In addition, a pre-trained AlexNet CNN is used to generate a public key. After XORing with the secret key, the latter is used, on the one hand, to generate the initials values and the control parameters of the ELCM. On the other hand, it will be split into two keys in order to generate a random grayscale image, which will then be XORed with the three components (i.e., R, G and B) of the color plaintext image. Afterward, a permutation operation, DNA encoding, diffusion operation as well as bit reversion operation are then applied to the R, G and B components. The performance evaluation demonstrates that the ELCM not only has an infinite chaotic parameter range, but also exhibits a high chaotic complexity. Besides, experimental results as well as security analysis confirm that with NPCR of 99.6287%, the designed color image encryption scheme achieves an average entropy of 7.9975 and a near zero correlation (-0.0027). Furthermore, the proposed encryption scheme is in fact of higher security level in comparison to other schemes recently presented in the literature, thus making it highly suitable for today's real-time applications. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones. [ABSTRACT FROM AUTHOR]

Encoding-decoding convolutional neural network structures have shown powerful performance in contour detection tasks, however, the design of models in the past seems to have little reference to physiological mechanisms, especially biological visual mechanisms. And physiological studies have shown that the visual system is efficient and accurate in the extraction of contour features. Meanwhile, when designing network models, researchers pay more attention to the impact of decoding networks on the performance of the models and pay little attention to the impact of encoding networks on the performance of the models. Therefore, in this paper, inspired by the effective mechanism of the biological vision system for contour detection, we combine it with convolutional neural net and self-attention mechanism to design a multi-level interaction contour detection model. We call it MI-Net. Among them, the multi-level interaction module is designed to simulate the transmission mechanism of visual information across the visual cortex and the feedback mechanism of visual information to realize the multi-level interaction of different levels of the encoding network, and then optimize each visual level to achieve the improvement of the performance of contour detection. The feature integration module is designed by simulating the feature integration function of the inferior temporal (IT) cortex. Multiple feature integration modules are combined into a decoding network to increase the feature integration capability of the decoding network. Experiments on publicly available datasets show that our proposed contour detection network has good performance. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) is an emerging technology with applications in the surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the rapid growth of ubiquitous Wi-Fi technologies and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) – a novel approach for HAR that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms – the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used Wavelet CNN – a deep convolutional network analogous to the well-studied ResNet and DenseNet networks, for activity classification. We evaluate the performance of our proposed PCWCNN algorithm on both line-of-sight data and non-line-of-sight data. We empirically show that our proposed PCWCNN algorithm performs very well on these real datasets, outperforming relevant state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Residual learning is one of the most effective components in blind image denoising. It learns to estimate the noise instead of the clean image itself. A shortcoming of residual learning is that it cannot capture hierarchical features efficiently because there are no connections between layers to extract multi-level features. Therefore, residual dense network (RDN) adds dense connections among layers in the residual block to facilitate extraction of hierarchical features. Although RDN is a superior non-blind denoiser, it is only better than state-of-the-art methods in blind denoising when noise level is high. In this paper, we employ the concept of Wiener filters and add a non-residual network as a guidance for RDN. The non-residual network predicts the signal instead of the noise to assist RDN in achieving satisfactory performance across all noise levels. Then, a guidance network combines the outputs from both residual and non-residual networks to generate the final denoised output. Experimental results show that the new architecture performs better than state-of-the-art blind denoisers quantitatively and image quality of the new method is also better than existing methods. • Improve performance of residual dense network in blind image denoising by employing the concept of Wiener filters. • Use non-residual network as a guidance of residual dense network and assist in predicting the signal instead of the noise. • Multiplications of noisy input and outputs from networks are used for signal and noise power prediction. • A guidance network combines input, outputs from networks, and the above multiplications to generate final denoised output. • Experimental results show residual dense network with non-residual guidance performs better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As an essential step in the early diagnosis of retinopathy, the blood vessels morphological attributes assist specialists to obtain pathological information efficiently. Most existing deep learning methods are based on U-shaped convolutional neural networks for the segmentation of blood vessels and have made substantial progress. However, the variance between vessel images remains challenging for segmentation algorithms, as demonstrated by poor cross-validation performance between different datasets. In this paper, a novel U-shaped deep convolutional network is proposed for retinal vessel segmentation, namely spiking neural P-type Dual-channel dilated convolutional network (SDDC-Net). We redesign the classical U-shaped convolution network based on the spiking neural P system computational mechanism for the first time. Distinct from the conventional convolutional neural network, SDDC-Net integrates the spiking neural P system convolutional neurons into the classic encoder-decoder architecture. We employ dilated convolution into an encoder, which improves both capabilities of perceiving more contexts and perceptual sensitivity of thin blood. We evaluate this model on three public datasets (DRIVE, STARE, CHASE_DB1), which indicates the more sensitive detection of thin vessels compared to most existing methods, showing higher sensitivity and F1 metrics. Compared to the baseline U-Net, our sensitivity, accuracy, and F1 score metrics on DRIVE dataset surpass by 10.66%, 1.73%, and 1.47% respectively. We also evaluate the effectiveness of spiking neural P system and dilated convolution in the ablation experiments, which demonstrates that accuracy increases with few drops in specificity. The cross-validation experiments show that our model has not only effective segmentation ability but also excellent generalization ability. [ABSTRACT FROM AUTHOR]

With the help of some modern image generative techniques, it is possible to generate or manipulate image or video contents without introducing any obvious visual artifacts. If these manipulated images/videos are abused, it probably has a huge negative impact on society and individuals. Thus, deepfake detection has attracted considerable attention in recent years. Although the existing methods can achieve good detection performance on high-quality datasets, they are still far from satisfactory for low-quality dataset and cross-dataset evaluation. In this paper, therefore, we propose a new CNN-based method via multi-scale convolution and vision transformer for deepfake detection. In the proposed model, we design a multi-scale module with dilation convolution and depthwise separable convolution to capture more face details and tampering artifacts at different scales. Unlike the traditional classification module, furthermore, we employ a vision transformer to further learn the global information of face features for classification. Extensive experiments demonstrate that in most cases the proposed method achieves better detection results on both high-quality and low-quality datasets compared with related modern methods, and the cross-dataset generalization capabilities of the proposed method are good. In addition, many ablation experiments are provided to verify the rationality of the proposed network. [ABSTRACT FROM AUTHOR]

Acoustic source localization in the spherical harmonic domain with reverberation has hitherto not been investigated extensively. When the joint direction-of-arrival (DOA) estimation is treated as a classification task, it needs a lot of different classes and large-scale dataset, which is time-consuming and less cost-effective. Thus, a novel learning-based method is proposed in this paper, which separately estimates azimuths and elevations by two independent convolution neural networks (CNNs) with residual blocks. First, a mapping matrix is adopted to transform the spherical harmonic function into the Kronecker product of two Vandermonde vectors. Based on the special structure, features dependent on azimuth and elevation, respectively, are extracted as inputs to neural networks. This can divide the joint DOA estimation into two sub-tasks completed by two independent CNNs with residual blocks, which can be realized in parallel. Furthermore, the performance of the model learned by CNNs with residual blocks is better than ordinary CNNs. Simulations are conducted both on the simulated and real speech data for evaluating the performance of the proposed method. The results show that the method has higher accuracy. Moreover, the method is time-saving and effectively reduces the computational complexity. [ABSTRACT FROM AUTHOR]

Arbitrary shape scene text detection is still a challenging task in the computer vision community. Compared with anchor-based methods, segmentation-based methods have advantages in some respects, which detect text instances by generating accurate text components. In this paper, we propose a novel text components extraction network for arbitrary shape scene text detection. It can detect different text components through two parallel branches. In the first branch, the feature redistribution module (FRM) is proposed to extract text-related features and filter non-text information. Then, these text-related features are aggregated to generate text boundary maps. These text-related features are also fed to the second branch to progressively generate the text kernel maps. The second branch consists of a modified Transformer decoder with a multi-level progressive supervision strategy. It aims to capture spatial details and establish the dependencies between different text regions. In this way, our method can generate accurate text components without additional geometric calculations. Thanks to the powerful Transformer decoder and efficient differentiable binarization module, our method not only achieves advanced detection accuracy but also has a competitive inference speed. Specifically, with the ResNet-18 backbone, our method can run at 43.5 FPS and achieve an F-measure of 83.3% on the Total-Text dataset, 1.43 times faster than the latest state-of-the-art method. With the ResNet-50 backbone, our method achieves an F-measure of 85.2% and outperforms the latest state-of-the-art method by 0.2% on the Total-Text dataset. Code is available at: https://github.com/As-David/TransText. [ABSTRACT FROM AUTHOR]

As moving objects always draw more attention of human eyes, the temporal motion information is always exploited complementarily with spatial information to detect salient objects in videos. Although efficient tools such as optical flow have been proposed to extract temporal motion information, it often encounters difficulties when used for saliency detection due to the movement of camera or the partial movement of salient objects. In this paper, we investigate the complementary roles of spatial and temporal information and propose a novel dynamic spatiotemporal network (DS-Net) for more effective fusion of spatiotemporal information. We construct a symmetric two-bypass network to explicitly extract spatial and temporal features. A dynamic weight generator (DWG) is designed to automatically learn the reliability of corresponding saliency branch. And a top-down cross attentive aggregation (CAA) procedure is designed to facilitate dynamic complementary aggregation of spatiotemporal features. Finally, the features are modified by spatial attention with the guidance of coarse saliency map and then go through decoder part for final saliency map. Experimental results on five benchmarks VOS, DAVIS, FBMS, SegV2, and ViSal demonstrate that the proposed method achieves superior performance than state-of-the-art algorithms. The source code is available at https://github.com/TJUMMG/DS-Net. [ABSTRACT FROM AUTHOR]

With the rapid development of convolutional neural networks (CNNs), the performance of human pose estimation has been significantly improved. However, state-of-the-art methods still face specific challenges, such as occluded keypoints and nearby persons. Unlike the classical network improvement strategy, our approach obtains adaptive data to obtain more accurate keypoints through data augmentation. In this paper, we propose Adaptive Data Augmentation Network (ADA-Net), which brings more adaptive training data by adding occlusion and interleaving information to the original image. First, we introduce Active Transmission Network (ATNet), which actively learns the transformation matrix from the original image and uses the matrix to synthesize new images for training. Second, we adopt an adversarial training strategy combined with the ATNet that allows us to capture more challenging cases. Extensive experiments show that our approach achieves comparable or even better results than most state-of-the-art methods. In particular, our ADA-Net outperforms High-Resolution Network (HRNet) by 1.1 and 0.7 points on the COCO test-dev set and MPII validation set, respectively. • The diversity of training samples will enhance the performance of human pose estimation. • The appropriate number of pasted samples on the original image can affect network performance. • Pasting the complete person is beneficial in enhancing the original image information. • The generative adversarial network brings the synthetic image closer to real challenging cases. [ABSTRACT FROM AUTHOR]

SAR images are a high-resolution map of surface target areas and terrain in the range and the cross-range dimension. Usually, moving targets appear as defocused and spatially displaced objects superimposed on the SAR map. In this paper, a new moving target refocusing imaging method based on Range Doppler (RD) Algorithm and convolutional neural network is proposed. Firstly, Range Doppler (RD) Algorithm is used to preprocess the echo data to obtain the blurring SAR image as the input data of convolution neural network. Secondly, according to SAR imaging characteristics, the original U-net structure is improved to extract the structure information of input data and provide good target reconstruction. The trained network can focus moving targets in case of different azimuth velocities. Finally, the experimental results prove the effectiveness of the proposed method in radar image focusing. [ABSTRACT FROM AUTHOR]