Image compressive sensing recovery based on weighted structure group sparse representation
Non-local similarity prior has been widely paid attention to efficiently improve image recovery quality.To fur-ther improve the recovered image quality for compressive sensing (CS),an image compressive sensing recovery method based on reweighted structure group sparse representation (WSGSR) was prop...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2017-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017041/ |
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| Summary: | Non-local similarity prior has been widely paid attention to efficiently improve image recovery quality.To fur-ther improve the recovered image quality for compressive sensing (CS),an image compressive sensing recovery method based on reweighted structure group sparse representation (WSGSR) was proposed.<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mi mathvariant="script">l</mi> <mn>1</mn> </msub> </math></inline-formula>-norm of WSGSR of image non-local similar patch group was used as a regularization term to optimize reconstruction,which achieved well reserving image high-frequency detail with less loss of image low-frequency component,and thus considerably improve the recon-structed image quality.A reweighted soft thresholding shrinkage method was deduced to achieve optimization solution,in which the significant coefficient with large magnitude value was shrunk by a small threshold,while the non-significant coefficient with small magnitude value was shrunk by a relative large threshold.Experimental results comparison demon-strate the effectiveness of the proposed method. |
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| ISSN: | 1000-436X |