A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to improve boundary adherence, which is further promoted by incorporating the boundary term into the distance calculation of the k-means clustering. Berkeley Segmentation Dataset BSDS500 is used to qualitatively and quantitatively evaluate the proposed BSLIC method. Experimental results show that BSLIC achieves an excellent compromise between boundary adherence and regularity of size and shape. In comparison with SLIC, the boundary adherence of BSLIC is increased by at most 12.43% for boundary recall and 3.51% for under segmentation error.

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.