A sparse gradients field based image denoising algorithm via non-local means

作者：Zhang Rui^*; Feng Xiang Chu; Wang Si Qi; Chang Li Hong

来源：Acta Automatica Sinica, 2015, 41(9): 1542-1552.

DOI：10.16383/j.aas.2015.c140903

• Forward-backward splitting algorithm
• Image denoising algorithm
• Non local means (NLM)
• Objective measurement
• Self-similarities
• Similarity measurements
• Sparse gradients field (SGF)
• Structural information

Non-local means (NLM) algorithm can obtain very good denoising results by making full use of the self-similarity and structural information's redundancy of images. But the weight function of NLM algorithm cannot accurately measure the similarity between image patches in the case of strong noise. Therefore, the weight function of NLM has been improved by using the gradients information of images in many papers. However, the traditional gradients operators cannot improve the accuracy of similarity measurement efficiently because they are sensitive to noise. This paper proposes a sparse gradients field (SGF) based image denoising algorithm via non-local means, in which the SGF of image is introduced to redefine the similarity measurement. First, a global sparse gradients field model and an adaptive sparse gradients field model are proposed which is different from traditional gradients operators and solved by forward-backward splitting algorithm. Then, the algorithm is proposed by redefining weight function via SGF. Experimental results demonstrate that compared with the NLM algorithm and other improved algorithms using information of gradients our proposed method has a better performance both in objective measurement and visual evaluation.

• 出版日期2015-9-1
• 单位西安电子科技大学