Abstract
Good learning image priors from the noise-corrupted images or clean natural images are very important in preserving the local edge and texture regions while denoising images. This paper presents a novel image denoising algorithm based on superpixel clustering and sparse representation, named as the superpixel clustering and sparse representation (SC-SR) algorithm. In contrast to most existing methods, the proposed algorithm further learns image nonlocal self-similarity (NSS) prior with mid-level visual cues via superpixel clustering by the sparse subspace clustering method. As the superpixel edges adhered to the image edges and reflected the image structural features, structural and edge priors were considered for a better exploration of the NSS prior. Next, each similar superpixel region was regarded as a searching window to seek the first L most similar patches to each local patch within it. For each similar superpixel region, a specific dictionary was learned to obtain the initial sparse coefficient of each patch. Moreover, to promote the effectiveness of the sparse coefficient for each patch, a weighted sparse coding model was constructed under a constraint of weighted average sparse coefficient of the first L most similar patches. Experimental results demonstrated that the proposed algorithm achieved very competitive denoising performance, especially in image edges and fine structure preservation in comparison with state-of-the-art denoising algorithms.
Highlights
As one of the most fundamental low-level vision problems, image denoising has been widely studied in computer vision, serving as the foundation and precondition for image processing, such as visual saliency detection, image segmentation, image classification, etc
It is known that better learning of the prior can improve the performance of Instead of clustering size-fixed patches that are inflexibly extracted from images, clustering denoising algorithms based on structure clustering and sparse representation
We presented a new image denoising algorithm which made full use of image priors, including the nonlocal self-similarity (NSS) prior in the spatial domain and sparse transform domain, edges and structural information, and sparsity
Summary
As one of the most fundamental low-level vision problems, image denoising has been widely studied in computer vision, serving as the foundation and precondition for image processing, such as visual saliency detection, image segmentation, image classification, etc. Though NSS in low-level vision cues has been widely utilized to improve image denoising performance in most existing methods, we argue that such utilizations of NSS are not sufficiently effective These methods learn NSS prior via clustering local size-fixed patches extracted from an image, which may neglect the image edge and structural features to some extent. This paper proposes an improved algorithm for image denoising by taking advantage of multiple priors to achieve a better denoising performance, including the NSS prior in the spatial domain and sparse transform domain, sparsity, structure, and edge prior. We first divided the image into multiple superpixels by the simple linear iteration clustering (SLIC) method and grouped superpixels to generate irregular regions by the sparse subspace clustering method with local features.
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