| Citation: | WU Qingbo, REN Wenqi. Structural weighted low-rank approximation for Poisson image deblurring[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1701-1710. doi: 10.13700/j.bh.1001-5965.2020.0061(in Chinese)
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To solve the problem of image quality degradation caused by Gaussian blur and Poisson noise, an image deblurring method based on structural weighted low-rank approximation is proposed. First, a structural transformation is introduced by subsequently combining the four basic operations of scaling, rotation, shearing, and flipping in order to boost the similarity of candidate patches in the searching space. Then, a novel objective function is proposed by carefully designing the regularization term. To this end, we perform structural transformation on image patches and then penalize the transformed results with Weighted Nuclear Norm (WNN) based on the assumption of low-rank among non-local similar patches, suppressing Poisson noise at the same time of deblurring. Finally, an alternating optimization algorithm is presented based on the Half-Quadratic Splitting (HQS) method to solve the proposed objective function for Poisson image deblurring. Experimental results demonstrate that, under multiple intensities of Poisson noise, the proposed algorithm achieves higher Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) than the state-of-the-art deblurring methods.
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