# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 1 Introduction
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. Moreover, in most existing methods, the NSS prior is usually exploited by searching for nonlocal similar patches to a local patch across a size-fixed square searching window, which always leads to massive workload and ignores the similarities between pairs of patches with large spatial distances. Since most existing methods do not make full use of NSS prior in low-level vision cues, it is necessary to learn the prior in mid-level vision cues for a better exploration of the NSS prior. With the above considerations, this paper proposes to further learn NSS in mid-level vision cues via superpixel clustering using the sparse subspace clustering method. Since the superpixel edges adhere to the image edges and reflect the image structural features, structural and edge information can be considered for a better exploration of the NSS prior by superpixel clustering. Furthermore, 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. In the proposed algorithm, 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. Regarding these regions as searching windows, we sought the first \(L\) most similar patches to each local patch. Next, a data-adaptability dictionary for each region was learned to obtain the initial sparse coefficients of local patches extracted from the images. Finally, to improve the effectiveness of the sparse coefficient for each patch, a weighted sparse coding model was constructed by adding a weighted average sparse coefficient of the first \(L\) most similar patches to the sparse representation model. Once final sparse coefficients for all patches were acquired, the noise-free image was obtained. Benefiting from two factors, the proposed algorithm achieves enhanced image denoising performance. First, learning the NSS in mid-level vision cues via superpixel clustering promotes a better exploitation of the NSS prior. Furthermore, regarding similar superpixel regions as a searching window can avoid the ignorance of similarities between pairs of patches with large spatial distances, which also contributes to the better exploitation of the NSS prior. Second, a weighted sparse coding model was established, which reduced the impact of noise on sparse representation and improved the accuracy of sparse coefficient for each patch. The rest of this paper is organized as follows. In Section 2, the basics of the proposed algorithm are described, including the SLIC algorithm, sparse subspace clustering algorithm and sparse representation algorithm. In Section 3, we introduce the proposed denoising model in detail. Next, experimental results and discussion are presented in Section 4. Finally, we make our conclusions in Section 5.