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

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 …

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

By integrating certain optimization solvers with deep neural networks, deep unfolding
network (DUN) with good interpretability and high performance has attracted growing …

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 …

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

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 …

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

Well-known compressed sensing (CS) is widely used in image acquisition and
reconstruction. However, accurately reconstructing images from measurements at low …