Low-rank approach for image nonblind deconvolution with variance estimation

	Low-rank approach for image nonblind deconvolution with variance estimation
	Yang, H.; G. S. Hu; Y. Q. Wang and X. T. Wu
	2015
发表期刊	Journal of Electronic Imaging
卷号	24 期号:6 页码:11
摘要	We develop a low-rank approach for image restoration by exploiting the image's nonlocal self-similarity. We assume that the matrix stacked by the vectors of nonlocal similar patches is of low rank and has sparse singular values. Based on this assumption, we propose a new image deconvolution algorithm that decouples the deblurring and denoising steps. Specifically, in the deblurring step, we involve a regularized inversion of the blur in the Fourier domain, which amplifies and colors the noise and corrupts the image information. Hence, in the denoising step, a singular-value decomposition of similar packed patches is used to efficiently remove the colored noise. Furthermore, we derive an approach to update the estimation of noise variance for setting the threshold parameter at each iteration. Experimental results clearly show that the proposed algorithm outperforms many state-of-the-art deblurring algorithms such as iterative decoupled deblurring BM3D in terms of both improvement in signal-to-noise-ratio and visual perception quality. (C) 2015 SPIE and IS&T
收录类别	SCI ; EI
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/55502
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Yang, H.,G. S. Hu,Y. Q. Wang and X. T. Wu. Low-rank approach for image nonblind deconvolution with variance estimation[J]. Journal of Electronic Imaging,2015,24(6):11.
APA	Yang, H.,G. S. Hu,&Y. Q. Wang and X. T. Wu.(2015).Low-rank approach for image nonblind deconvolution with variance estimation.Journal of Electronic Imaging,24(6),11.
MLA	Yang, H.,et al."Low-rank approach for image nonblind deconvolution with variance estimation".Journal of Electronic Imaging 24.6(2015):11.

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