image denoising algorithm combined with sgk dictionary learning and principal component analysis noise estimation

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ID: 253214
2018
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Abstract
SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.
Reference Key
zhao2018mathematicalimage Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Wenjing Zhao;Yue Chi;Yatong Zhou;Cheng Zhang
Journal journal of power sources
Year 2018
DOI
10.1155/2018/1259703
URL
http://dx.doi.org/10.1155/2018/1259703
Keywords
civil engineering (general)mathematics engineering (general)

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