Noise removal plays an important role in image processing. Traditional image denoising method is wavelet denoising and get better performance but can loss details and lead to edge blurring. An image denoising method based on (Morphogonal Component Analysis) MCA and K-SVD algorithm is carried out in this paper to overcome above drawbacks. Considering the demand of the image sparsity is higher via traditional MCA. This paper endeavors to search the optimal minimum solution of l1-norm and the suboptimal solutions which are close to the minimum l1-norm, and then weighted sum of these two parts is taken as the estimation of the sources. This method improves the performance of high demand to image sparsity . In this paper, first we decompose the image into structure image, texture image and edge image by improved MCA . Second denoise the structure part via wavelet technique and the other two parts are processed by improved K-SVD. Simulation results show that our method can get better denoising performance both in PSNR value and visual effects.
