%0 Electronic Article
%A Yunjiao Bai
%A Yi Liu
%A Quan Zhang
%A Lina Jia
%A Zhiguo Gui
%K nonlocal total variation model
%K highly degenerated images
%K preprocessed image
%K BM3D algorithm
%K denoising performance
%K three-dimensional filtering algorithm
%K weight function
%K split Bregman algorithm
%K NLTV model
%K block-matching
%K energy functional
%K NLTV regularisation term
%K image denoising
%X 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.
%T Image denoising via an improved non-local total variation model
%B The Journal of Engineering
%D August 2018
%V 2018
%N 8
%P 745-752
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=867sxc4lknon.x-iet-live-01content/journals/10.1049/joe.2017.0388
%G EN