Motivated by the superior performance of nonconvex nonsmooth L p (0< p< 1) norm, this
paper introduces a novel method that combines the weighted Schatten p-norm, L p-norm …

To improve the image segmentation quality, it is important to adequately describe the local
features of targets in images. In this paper, we develop a novel adaptive total variation …

Regularization-based methods are commonly used for image registration. However, fixed
regularizers have limitations in capturing details and describing the dynamic registration …

Blind image deconvolution has attracted growing attention in image processing and
computer vision. The total variation (TV) regularization can effectively preserve image …

This article proposes a two-stage image segmentation method based on the MS model,
aiming to enhance the segmentation accuracy of images with complex structure and …

Image denoising is a very important problem in image processing field. In order to improve
denoising effects and meanwhile keep image structures, a novel weighted total variation …

This study introduces a non-convex fractional-order hyper-Laplacian variational model for
Gaussian noise removal. It employs first the primal-dual hybrid gradient algorithm to solve …

A non‐convex ternary variational decomposition model is proposed in this study, which
decomposes the image into three components including structure, texture and noise. In the …

In this paper, we propose a novel model for the decomposition of cartoon–texture images,
which integrates the edge-aware weighted least squares (WLS) with low-rank regularization …

A nonconvex total variation image denoising model with a double regularization penalty
term is proposed in this paper, which effectively overcomes the shortcomings of a single …