According to the theory of sparse representation, images can be sparse-represented by using an appropriately redundant dictionary. The completeness can enable using very few big coefficients to capture the important information of images, and cause more robust to noise. Regarding image de-noising, considering the human visual characteristics, this paper studied the effective representation of characteristics and edge information of noisy image based on complete dictionary. For more effective feature retaining of images, a method of feature-retaining de-noising via sparse representation was proposed, which made the Structural SIMilarity (SSIM) as fidelity measure of the information. The experimental results indicate that the proposed algorithm has a better efficiency of de-noising, enhances the capacity of retaining feature, and gets a better visual effect of de-noised image.