Though the sparse representation has demonstrated to be a very effective tool to de-noise the images with low levels of noise, it usually losses the power to well preserve structural features in images with high levels of noise. In this paper, we propose an improved de-noising method for images with low signal-to noise ratio. Specifically, the proposed method takes the histogram structural similarity (HSSIM) as similarity factor to replace the reconstruction error as the new fidelity term, and finds the most appropriate sparse coefficients by using the modified orthogonal matching pursuit (OMP) algorithm which enables structures in the reconstructed image run as close as possible to the ideal image. In addition, the proposed method adaptively trains the initialized dictionary by using the K-singular value decomposition (K-SVD) algorithm based on HSSIM to assure the image structures can be well reconstructed under the high noise circumstance. Experiment results have shown that the proposed method is better than some well-known de-noising methods in terms of PSNR and edge-preserved index (EPI) in high noise condition.
