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Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage

School of Physics and Information Engineering, Key Lab of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou 363000, China
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Algorithms 2019, 12(10), 221; https://doi.org/10.3390/a12100221
Received: 17 September 2019 / Revised: 16 October 2019 / Accepted: 21 October 2019 / Published: 22 October 2019
Image deblurring under the background of impulse noise is a typically ill-posed inverse problem which attracted great attention in the fields of image processing and computer vision. The fast total variation deconvolution (FTVd) algorithm proved to be an effective way to solve this problem. However, it only considers sparsity of the first-order total variation, resulting in staircase artefacts. The L1 norm is adopted in the FTVd model to depict the sparsity of the impulse noise, while the L1 norm has limited capacity of depicting it. To overcome this limitation, we present a new algorithm based on the Lp-pseudo-norm and total generalized variation (TGV) regularization. The TGV regularization puts sparse constraints on both the first-order and second-order gradients of the image, effectively preserving the image edge while relieving undesirable artefacts. The Lp-pseudo-norm constraint is employed to replace the L1 norm constraint to depict the sparsity of the impulse noise more precisely. The alternating direction method of multipliers is adopted to solve the proposed model. In the numerical experiments, the proposed algorithm is compared with some state-of-the-art algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), signal-to-noise ratio (SNR), operation time, and visual effects to verify its superiority. View Full-Text
Keywords: fast total variation deconvolution; total generalized variation; non-convex shrinkage; Lp shrinkage; direction method of multipliers; image restoration fast total variation deconvolution; total generalized variation; non-convex shrinkage; Lp shrinkage; direction method of multipliers; image restoration
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Lin, F.; Chen, Y.; Chen, Y.; Yu, F. Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage. Algorithms 2019, 12, 221.

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