Image denoising via improved sparse coding

This paper presents a novel dictionary learning method for image denoising, which removes zero-mean independent identically distributed additive noise from a given image. Choosing noisy image itself to train an over-complete dictionary, the dictionary trained by traditional sparse coding methods contains noise information. Through mathematical derivation of equation, we found that a lower bound of dictionary is related with the level of noise in dictionary learning. The proposed idea is to take advantage of the noise information for designing a sparse coding algorithm called improved sparse coding (ISC), which effectively suppresses the noise influence for training a dictionary. This denoising framework utilizes the effective method, which is based on sparse representations over trained dictionaries. Acquiring an over-complete dictionary by ISC mainly includes three stages. Firstly, we utilize K-means method to group the noisy image patches. Secondly, each dictionary is trained by ISC in corresponding class. Finally, an over-complete dictionary is merged by these dictionaries. Theory analysis and experimental results both demonstrate that the proposed method yields excellent performance.