Image denoising via expected patch log likelihood with Gaussian model identification
Yibin Tang, Yan Zhang, Ying Chen, Yuan Gao, Changping Zhu · 2016
In this paper, a new strategy is proposed for the existing expected patch log likelihood (EPLL) algorithm to deal with image denoising, where the Gaussian model identification is incorporated to improve the likelihood estimation for patches. In detail, the noisy patches are first divided into two categories, i.e., smooth and unsmooth groups. Sequentially, in the iteration of the likelihood estimation, these two groups are separately performed via the Gaussian models with the corresponding covariance parameters. Experiments show that, with this Gaussian model identification for patches, the proposed method can achieve the better performance than the traditional EPLL algorithm.