Rician noise removal via a learned dictionary

Jian Lu, Jiapeng Tian, Lixin Shen, Qingtang Jiang, Xueying Zeng, Yuru Zou

Abstract: This paper proposes a new effective model for denoising images with Rician noise. The sparse representations of images have been shown to be efficient approaches for image processing. Inspired by this, we learn a dictionary from the noisy image and then combine the MAP model with it for Rician noise removal. For solving the proposed model, the primal-dual algorithm is applied and its convergence is studied. The computational results show that the proposed method is promising in restoring images with Rician noise.

Journal: Mathematical Problems in Engineering. Volume 2019, Article ID 8535206, 13 pages

DOI: 10.1155/2019/8535206