Low-rank approach for image nonblind deconvolution with variance estimation

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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.