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

Abstract

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.

Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization

“…The TV regularization method imposes a TV regularization constraint on the image gradients to recover the smooth regions and preserve the edges of the image. Common TV varieties include overlapping group sparse TV [17,18], higher-order TV [19], total generalized variation [20], anisotropic total p-variation [21,22] and fractional order regular constraint [23,24] etc.…”