Image Denoising Algorithm Based on Kernel Sparse Representation

Abstract

The traditional denoising algorithm has residual noise after removing noise,and image denoising effect is not obvious for the large noise.Aiming at this problem,a new image denoising algorithm is proposed in this paper.In this algorithm,the input image with noise can be split into overlapped image patches.Through randomly selecting moderate image block to learn,an adaptive redundant dictionary can be got.Then the sparse representation coefficients can be obtained from this redundant dictionary with nuclear regularized orthogonal matching pursuit technology.Then the image can be restored by these coefficients.Experimental results show that compared with K-Singular Value Decomposition(K-SVD) algorithm,the Peak Signal to Noise(PSNR) of the proposed algorithm is better,the image detail and texture information can be well preserved.