Image denoising via an improved non-local total variation model

The Journal of Engineering

Yunjiao Bai¹  Quan Zhang²  Yi Liu³ 

[1] Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China , Taiyuan 030051 , People'State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology , Beijing 100081 , People's Republic of China

关键词: image denoising;    three-dimensional filtering algorithm;    NLTV regularisation term;    preprocessed image;    highly degenerated images;    weight function;    denoising performance;    split Bregman algorithm;    energy functional;    NLTV model;    BM3D algorithm;    block-matching;    nonlocal total variation model;   

DOI  :  10.1049/joe.2017.0388

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

This study presents an improved non-local total variation (NLTV) model by using the block-matching and three-dimensional filtering (BM3D) algorithm for image denoising. First, the preprocessed image is obtained with the BM3D algorithm. Then, taking the place of the noisy image, the preprocessed image is used to construct the fidelity term of the energy functional and calculate the weight function in NLTV regularisation term. Finally, the energy functional is solved by the split Bregman algorithm. Experimental results demonstrate that the proposed model achieves better denoising performance than the original NLTV model in the visual appearance and objective indices, especially for the highly degenerated images. In addition, the proposed model can effectively suppress the appearance of the false information in the flat region, which overcomes the problem faced by the BM3D algorithm.

【 授权许可】

CC BY