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Block matching 3D denoising (BM3D) is an excellent single-image denoising method. However, it still needs to be improved for solving practical problems. In this study, the authors attempt to improve the method of BM3D. First, one of the problems of BM3D is that some of its references cannot perform self-adaption when the noise intensity of the images is changed. Therefore, they propose a method using total variation (TV) to calculate the image noise intensity and make the references perform self-adaption. Second, finding similar blocks in the BM3D method is a time-consuming procedure. To solve this problem, they analyse the relationship between the numbers of similar blocks and denoising effect, improve the process of searching for similar blocks, and reduce the running time. Third, through the experiment they find that the denoising effect of BM3D method in the domain of complex texture is unsatisfactory. Thus, they proposed a hybrid denoising method for the complex texture area, using the new TV model and BM3D method together to restore the image. Their experimental results show that the improved BM3D method performs better than the original BM3D method.

An improved multi-constraint stereo matching strategy is proposed herein. First, the original reference images are downsampled to reduce the matching scale. Absolute difference and rank transform are used as the matching cost. We establish the associated disparity map and the matching cost matrix to constrain the matching process. We use the associated disparity map to apply a smoothing constraint on disparity values. We then apply a uniqueness constraint using the associated disparity map and the matching cost matrix. We also apply a continuity constraint to improve the efficiency of the algorithm. Second, for the downsampled disparity map, the disparity map of the original image is obtained by interpolation and filling. Lastly, the final disparity map is obtained using a sub-pixel refinement strategy. The experimental results demonstrate that the algorithm is obviously superior to the traditional method. In addition, a multi-constraint matching process may be extracted as a framework for combination with other algorithms. Compared with the original algorithms, the fusion algorithm improved matching accuracy by 3% on average.

Reduce the expected number of modifications per pixel (ENMPP) of cover image is a useful way to improve the imperceptibility in steganography. Many works related with this issue have been proposed in recent years. In this paper, a scheme of LSB steganography is proposed which based on concept of finding optimization pixels selection from cover image. The experimental results show that effect of reducing ENMPP is remarkable.