Image Denoising Based on Dictionary Learning of Mean Corrected Atoms

Image denoising is an important pre-processing step in image processing. Different algorithms have been proposed in past few decades with varying denoising performances. In recent years, the image-based sparse representation can better characterize the essential features of images. Image sparse representation based on adaptive overcomplete dictionary can satisfy the sparsity and image noise separability, and has been successfully applied to image denoising, forming a kind of image denoising algorithms based on sparse representation. K-singular Value Decomposition algorithm is currently the most representative, the most widely applied adaptive learning dictionary image denoising algorithm. Based on the K-Singular Value Decomposition algorithm, this paper proposes an image denoising algorithm based on dictionary learning of mean correction atoms. In the dictionary initialization process, the dictionary learning scheme of mean corrected atoms is proposed to effectively suppress noisy atoms. The experimental results show that this algorithm can obtain better image restoration quality than these similar algorithms.