Non-local clustering via sparse prior for sports image denoising

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ying Zhang
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引用次数: 0

Abstract

Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image into the denoising process, a non-local clustering image denoising algorithm is proposed. A sparse representation dictionary is obtained by combining the image blocks of external clean image and internal noise image. The sparse coefficient estimation of ideal image is obtained by global similar block matching. Based on the class dictionary and the estimated sparse coefficient, a sparse reconstruction method based on compressed sensing technology is used to denoise the image. Experimental results show that compared with traditional image denoising methods, the proposed algorithm can significantly reduce the denoising block effect and preserve more details while transitioning more naturally in the flat area of the image.
基于稀疏先验的非局部聚类运动图像去噪
图像去噪是图像预处理中的一个重要环节。为了将外部干净图像的先验信息引入到去噪过程中,提出了一种非局部聚类图像去噪算法。将外部干净图像的图像块与内部噪声图像的图像块进行组合,得到稀疏表示字典。通过全局相似块匹配得到理想图像的稀疏系数估计。基于类字典和估计的稀疏系数,采用基于压缩感知技术的稀疏重建方法对图像进行去噪。实验结果表明,与传统的图像去噪方法相比,该算法可以显著降低去噪块效应,保留更多细节,同时在图像的平坦区域更自然地过渡。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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