Most traditional algorithms for compressive sensing image reconstruction suffer from the
intensive computation. Recently, deep learning-based reconstruction algorithms have been …

In this paper, we propose, analyze, and test a new method for solving ℓ 1-norm
regularization problems arising from the spare solution recovery in compressed sensing …

Multimode fiber (MMF) imaging is an emerging field of fiber imaging technology in the last
few decades. However, its high sensitivity to dynamic perturbance limits its practical …

Deep learning-based Compressed Sensing (CS) reconstruction attracts much attention in
recent years, due to its significant superiority of reconstruction quality. Its success is mainly …

We demonstrate all-fiber reflective single-pixel imaging with a long working distance and
large field of view. The wavelength-dependent multimode fiber speckle is generated to …

This paper focuses on some customized applications of the proximal point algorithm (PPA)
to two classes of problems: the convex minimization problem with linear constraints and a …

It is a challenge task to reconstruct images from compressed sensing measurement due to
its implicit ill-posed property. In this paper, we propose an image reconstruction algorithm for …

Due to the restriction of the scanning environment and the energy of X-ray, few projections of
an object can be obtained in some practical applications of computed tomography (CT). In …

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction,
which utilizes high-order image derivatives, is superior to total variation-based methods in …

Purpose: Metal artifact reduction (MAR) is a major problem and a challenging issue in x‐ray
computed tomography (CT) examinations. Iterative reconstruction from sinograms …