No abstract

In recent years, sparse representation theory and low-rank approximation model have been widely used in signal and image processing fields. In the study of natural image denoising, non-local similarity method can enhance the correlation of grouped image blocks, and a low rank prior is used to devise a bilateral sparse representation of the image matrix, consequently achieving the purpose of removing additive white Gaussian noise. However, problems such as how to select thresholds after a singular value shrinkage, and how to eliminate image artifacts in removing noise especially with high levels, have not been resolved. In this paper, a low rank adaptive singular value thresholding (ASVT) denoising algorithm based on singular value decomposition(SVD) is proposed. Our method uses random matrix and asymptotic matrix reconstruction theory to scientifically select the threshold of singular value thresholding. At the same time, a dual-domain filtering method is used to process the visual artifacts after image denoising by ASVT, which we call collaborative singular value thresholding (CSVT) algorithm. The experimental results show that the proposed algorithm has a certain improvement in subjective visual effects, and objective quantitative indicators compared with some related advanced denoising algorithms. INDEX TERMS Image denoising; Image enhancement; Low-rank approximation; Random matrix; Thresholding optimization; Dual-domain filtering.

In the traditional active contour models, global region-based methods fail to segment images with intensity inhomogeneity, and local region-based methods are sensitive to initial contour. In this study, a novel fuzzy energy-based active contour model is proposed to segment medical images, which are always corrupted by intensity inhomogeneity. In order to deal with intensity inhomogeneity, a local energy term is first constructed by substituting a non-local weight for Gaussian kernel widely used in traditional local region-based models. Second, the defined adaptive force can drive the level set function to adaptively increase or decrease according to image intensity information. Therefore, the initial contour can be initialised as a constant function, which eliminates the problem caused by contour initialisation. Moreover, the proposed active contour model is a convex function. Thus, the problem, resulting from optimising a non-convex functional in the traditional active contour models, can be avoided. Experimental results validate the superiorities and effectiveness of the proposed model for image segmentation with comparisons of those yielded by several state-of-the-art techniques.

No abstract