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Unveiling the dense correspondence under the haze layer remains a challenging task, since the scattering effects result in less distinctive image features. Contrarily, dehazing is often confused by the airlight-albedo ambiguity which cannot be resolved independently at each pixel. In this paper, we introduce a deep convolutional neural network that simultaneously estimates a disparity and clear image from a hazy stereo image pair. Both tasks are synergistically formulated by fusing depth information from the matching cost and haze transmission. To learn the optimal fusion of depth-related features, we present a novel encoder-decoder architecture that extends the core idea of attention mechanism to the simultaneous stereo matching and dehazing. As a result, our method estimates high-quality disparity for the stereo images in scattering media, and produces appearance images with enhanced visibility. Finally, we further propose an effective strategy for adaptation to camera-captured images by distilling the cross-domain knowledge. Experiments on both synthetic and real-world scenarios including comparisons with state-of-the-art methods demonstrate the effectiveness and flexibility of our approach. [ABSTRACT FROM AUTHOR]

The sensor response function of a color camera is very essential to understand an overall camera imaging pipeline and to process captured images. It is also true when we characterize underlying imaging behaviors of the electron multiplying charge coupled device (EMCCD) camera, which was recently proposed to acquire color images in low-light-level conditions. Unlike existing CCD cameras, the EMCCD camera contains partially uni-modal spectral sensitivity functions (SSFs), some of which consists of two base (bi-modal) functions, thus often leading to a serious color distortion in existing characterization techniques. To address this problem, we propose a novel method that corrects output colors of the EMCCD camera by analyzing its partially uni-modal characteristics. Specifically, our goal is to correct such color distortion by adjusting bi-modal SSFs to be uni-modal. We remove the secondary region of bi-modal channels using a pre-calculated spectral sensitivity of the EMCCD camera. Experimental results demonstrate that the proposed method reduces the color distortion as well as enlarges a color gamut, which is crucial to color reproduction. [ABSTRACT FROM AUTHOR]

Image-based pattern recognition techniques have attracted much attention in vision-based applications. Color-based methods have shown several benefits. However, due to color variations resulting from illumination changes, many color-based techniques have yet to demonstrate stable performance. For illumination-robust pattern recognition, we propose an image color registration method based on an image acquisition model. Since the image acquisition model is created using the variables related to an illumination condition, camera characteristics, and an object's surface reflectance, the proposed method normalizes the image's color by taking into account both the illumination and camera characteristics. To evaluate the performance of the proposed method in terms of illumination-robust pattern recognition, we perform both an image similarity test and a feature similarity test between images acquired under different illumination conditions. Through the experiments, the superiority and the usefulness of the proposed method was validated. [ABSTRACT FROM AUTHOR]

The inconsistency between the temporal motion vector and the disparity vector fields causes a drastic loss in coding performance during multiview video coding. In order to resolve this inconsistency problem, we investigate a new approach for motion vector coding. In this paper, degradation in accuracy using a conventional method is experimentally analyzed using mathematical tools. Then, we introduce a new framework for motion vector coding, including modified Inter-mode and Adaptive Direct-mode, in which an advanced motion vector prediction method is employed. The main idea of the proposed motion vector prediction method is to separate each motion vector field from the others. For this purpose, we introduce three alternative motion vectors; a virtual disparity vector, a virtual temporal motion vector and a scaled motion vector. The experimental results show that the proposed method can resolve the inconsistency problem and consequently improve the overall rate-distortion performance. The average bit-rate savings is about 7.3% compared to JMVC 6.0. At maximum, the gain obtained is about 20%, corresponding to a PSNR of 1.42 dB. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust corner detection method to improve both detection rate and localization accuracy by modifying the structure tensor-based corner detection method in two ways. First, we introduce a connected component analysis (CCA) method for constructing a CCA structure tensor in order to make the structure tensor adaptive to the structure of the image. Second, the normalized cross-correlation (NCC) method is applied for false corner rejection with the observation that the patch of a true corner has a distinctive characteristic compared with connected neighboring patches. The proposed method is compared with several corner detection methods over a number of images. Experimental results show that the proposed method shows better performance in terms of both detection rate and localization accuracy. [ABSTRACT FROM AUTHOR]

Stereoscopic video create the impression of depth by using two slightly different viewpoints. Conventional video quality assessment methods for 2D video are not suitable for stereoscopic video, so a new video quality assessment model for stereoscopic video is needed. In this paper, we propose a new objective video quality metric for compressed stereoscopic video. The proposed algorithm uses blocking artifacts, blurring in edge regions and video quality difference between two views. Blocking artifacts and blurring in edge regions are distortion appeared in the compressed video, and they are widely used in conventional video quality model. Difference in video quality between two views considers 3D effect of stereoscopic video. To verify the performance of the proposed algorithm, we performed subjective evaluation of stereoscopic video, and we confirmed that the proposed algorithm is superior to the conventional algorithms in respect to correlation with the subjective evaluation. [ABSTRACT FROM AUTHOR]

One of the important issues of green mobile networking is the low energy consumption for either mobile devices or transmissions. To adapt this, a low-cost Inter frame mode decision (MD) algorithm is proposed for H.264/AVC encoder to reduce the computational complexity of the original encoding procedure in this paper. The information extracted from macroblock (MB), such as energy, temporal domain mode similarity and so on, which can be used to pre-estimate the optimal mode of the MB is investigated and utilized to eliminate the redundant mode candidates. The performance evaluations including quantitative analysis and PC simulations show that the proposed algorithm is an energy-efficient source coding because it can reduce around 85% Inter frame encoding time with little quality loss. It can be widely implemented in green mobile networking systems with H.264/AVC standard to realize the real-time video signal coding. [ABSTRACT FROM AUTHOR]

A multiview sequence coding technique is proposed in this paper. A multiview sequence encoder must be able to reduce redundancies in both the time and view domains because the amount of data increases as the number of views (or cameras) increases. We define a new coding structure, a group of a group of pictures (GGOP), which is compatible with MPEG-2 and is flexible with a baseline distance. The GGOP could have several possible types, e.g., one-I type, two-I type, etc., according to the number of reference frame sequences. This permits one of these types to be selected according to the baseline distance among the cameras. In addition, the GGOP is designed for MPEG-2 compatibility. The proposed multiview sequence encoder consists of preprocessing stages, disparity estimation/compensation, motion estimation/compensation, residual coding, rate control, and entropy coding. It generates two types of bit streams, the main bit stream and the auxiliary bit stream. The main bit stream contains information concerning reference sequences including I-pictures, in order to maintain MPEG-2 compatibility. The auxiliary bit stream contains information concerning the remaining multiview sequences except for the sequences that include I-pictures. The proposed encoder shows improvements in compression ratio as well as in peak signal-to-noise ratio (PSNR) compared with conventional methods. The dependency of the GGOP on the baseline distance between cameras is also confirmed. [ABSTRACT FROM AUTHOR]

The goal of this paper is to determine dense disparity and motion fields jointly and efficiently, given a sequence of stereoscopic images. At each time instant, the motion field of left sequences is estimated by using the current disparity field, the next disparity field, and the motion field of right sequences. The improved concepts for the proposed joint estimation are based on an edge-preserving regularization, an ambiguous vector region detection, and a median filtering for block-based vector generation. The reported approach is verified by processing a set of stereo sequences. Results are given with real stereoscopic data. [ABSTRACT FROM AUTHOR]

This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

A constrained disparity estimation method is proposed which uses a directional regularization technique to efficiently preserve edges for stereo image coding. The proposed method smoothes disparity vectors in smooth regions and preserves edges in object boundaries well, without creating an oversmoothing problem. The differential pulse code modulation (DPCM) technique for disparity map coding is used prior to entropy coding, in order to improve the overall coding efficiency. The proposed disparity estimation method can also be applied to intermediate view reconstruction. Intermediate views between a left image and a right image provide reality and natural motion parallax to multiviewers. Intermediate views are synthesized by appropriately exploiting an interpolation or an extrapolation technique according to the characteristics of each region after identifying the regions as occluded regions, normal regions, and regions having ambiguous disparities. The experimental results show that the proposed disparity estimation method gives close matches between a left image and a right image and improves coding efficiency. In addition, we can subjectively confirm that the application of our proposed intermediate view reconstruction method leads to satisfactory intermediate views from a stereo image pair. [ABSTRACT FROM AUTHOR]