The goal of this paper is to give an introduction to analysis of images acquired by a digital
camera with visible illumination and to review its applications as a Process Analytical …

Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

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

With the aim of developing a fast yet accurate algorithm for compressive sensing (CS)
reconstruction of natural images, we combine in this paper the merits of two existing …

Depth image denoising is increasingly becoming the hot research topic nowadays, because
it reflects the three-dimensional scene and can be applied in various fields of computer …

In remote sensing applications and medical imaging, one of the key points is the acquisition,
real-time preprocessing and storage of information. Due to the large amount of information …

In the study of compressed sensing (CS), the two main challenges are the design of
sampling matrix and the development of reconstruction method. On the one hand, the …

Alternating direction method of multiplier (ADMM) is a widely used algorithm for solving
constrained optimization problems in image restoration. Among many useful features, one …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple
frames are mapped into a single measurement, with video compressive imaging and …