Sparse denoising algorithm is advantageous in optimizing the denoising effect but is inefficient because of its complex matrix operations in the sparse decomposition and dictionary training stages.Although classification is applied in the dictionary training stage

the method can still be enhanced.An improved algorithm is proposed to solve problems of inefficiency caused by complex matrix operations and global searching of the dictionary in the sparse decomposition stage. First

a local orthogonal matching pursuit algorithm

which introduces dictionary clustering based on orthogonal matching pursuit to generate sub-dictionaries

is proposed.Another novel element of this work is that a neighbor-prioritizing method

which selects optimal sub-dictionaries as matching space to sparse decompose

is proposed to optimize the denoising effect.Finally

the content cluster of the noisy image is denoised using the neighbor local K-SVD algorithm based on the clustering-based denoising method. Experiments on several images in the USC standard image library show that the proposed method leads to better denoising effect than that of other algorithms.The peak signal-to-noise ratio of the proposed algorithm is 1.53 dB higher than that of the K-SVD algorithm

0.72 dB higher than that of the BM3D algorithm

and 0.5 dB higher than that of the CSR algorithm on average.The running time of this algorithm is faster than that of the original algorithm. The proposed algorithm improves the effect and efficiency of gray image denoising and presents certain popularization value on gray images with much detail and texture.