Image de-noising based on learned dictionary

The sparse decomposition of image based on over complete dictionary is a new theory of image representation. Using the redundancy of over-complete dictionary, we can effectively capture various structural features of the image capture in order to effectively express the image. For sparse representation, theory research mainly focuses on sparse decomposition algorithm and dictionary structure algorithm. In this paper, an improved KSVD dictionary training method is proposed. By the method, an updated dictionary is acquired by smooth I₀ norm as the sparse coding method, and application in the sparse decomposition of image. Estimating the image by maximum a posteriori criterion was used to image de-noising, the experimental results confirm the proposed method is effectiveness.