Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (18): 177-185.DOI: 10.3778/j.issn.1002-8331.1908-0154

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Research on Image Denoising Algorithm Based on Non-local Clustering with Sparse Prior

YUAN Xiaojun, ZHOU Tao, LI Chen   

  1. 1.School of Eletronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2.Shanghai Integrated Circuit Research and Development Center Co., Ltd., Shanghai 201203, China
  • Online:2020-09-15 Published:2020-09-10



  1. 1.上海交通大学 电子信息与电气工程学院,上海 200240
    2.上海集成电路研发中心有限公司,上海 201203


Image denoising is a typical ill-posed problem, the latent clean image corrupted by noise is unknow, to solve the problem, prior information of the image should be considered in the target function. In order to use the prior information of external clean image for image denoising, an image denoising algorithm based on non-local clustering with external clean image and internal noise image is proposed. The patches of external clean image are combined with the internal noise patches to obtain a clustering sparse dictionary. A global block matching method is adopted to obtain the estimation of sparse coefficients of the ideal image. Based on clustering dictionary and estimated sparse coefficients, image denoising is done by sparse reconstruction based on compressed sensing technology. The experimental results indicate that the proposed method provides a better ability on suppression of the mosaic effect caused by the partition operation, preserve more details of image and is visually more pleasant. The combination of ideal image priori source further improves the ability of the algorithm, making it suitable for image denoising task even under heavy-noise deterioration.

Key words: sparse prior, nonlocal clustering, image denoising, heavy noise



关键词: 稀疏化先验, 非局域聚类, 图像去噪, 强噪声