The compressive sensing (CS) scheme exploits many fewer measurements than suggested
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …

Recent works that utilized deep models have achieved superior results in various image
restoration (IR) applications. Such approach is typically supervised, which requires a corpus …

Rank minimization methods have achieved promising performance in various image
processing tasks. However, there are still two challenging problems in the existing works …

Group-matrix based prior modeling has demonstrated superior performance in various
image restoration (IR) applications. Though the joint prior of nonlocal self-similarity (NSS) …

Image cartoon-texture decomposition is an important problem in image processing. In recent
years, by exploiting low-rank priors of images, low-rank minimization methods have been …

Recently, low-rank matrix recovery theory has been emerging as a significant progress for
various image processing problems. Meanwhile, the group sparse coding (GSC) theory has …

Impulsive noise is always present in real-world image Compressive Sensing (CS)
acquisition systems, where existing CS reconstruction performance may seriously …

Objective Photon-counting CT (computed tomography) has aroused more attention. The
relatively high dose of X-ray increases concerns about radiation, dose reduction can …

Image restoration is a fundamental problem of image processing. In recent years, low-rank
minimization (LRM) methods have been extensively applied to this problem to improve the …

Image prior plays a decisive role in the performance of widely studied model‐based
restoration methods. To further improve restoration performance, this paper proposes an …