Total Variation (TV) regularization has evolved from an image denoising method for images
corrupted with Gaussian noise into a more general technique for inverse problems such as …

The amount of noise included in a hyperspectral image limits its application and has a
negative impact on hyperspectral image classification, unmixing, target detection, and so on …

In the past decade, sparse and low-rank recovery has drawn much attention in many areas
such as signal/image processing, statistics, bioinformatics, and machine learning. To …

Variational models with \ell_1-norm based regularization, in particular total variation (TV)
and its variants, have long been known to offer superior image restoration quality, but …

We extend the alternating minimization algorithm recently proposed in Y. Wang, J. Yang, W.
Yin, and Y. Zhang, SIAM J. Imag. Sci., 1 (2008), pp. 248–272; J. Yang, W. Yin, Y. Zhang, and …

Nonconvex nonsmooth regularization has advantages over convex regularization for
restoring images with neat edges. However, its practical interest used to be limited by the …

The total variation (TV) model is attractive in that it is able to preserve sharp attributes in
images. However, the restored images from TV-based methods do not usually stay in a …

This paper addresses the issue of robust sparse recovery in compressive sensing (CS) in
the presence of impulsive measurement noise. Recently, robust data-fitting models, such as …

The restoration of blurred images corrupted with impulse noise is a difficult problem which
has been considered in a series of recent papers. These papers tackle the problem by using …

In this paper, we propose a two-phase approach to restore images corrupted by blur and
impulse noise. In the first phase, we identify the outlier candidates—the pixels that are likely …