Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

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 …

Recent deep network-based compressive sensing (CS) methods have achieved great
success. However, most of them regard different sampling matrices as different independent …

Deep unfolding network (DUN) that unfolds the optimization algorithm into a deep neural
network has achieved great success in compressive sensing (CS) due to its good …

Non-local self-similarity in natural images has been verified to be an effective prior for image
restoration. However, most existing deep non-local methods assign a fixed number of …

In order to improve CS performance of natural images, in this paper, we propose a novel
framework to design an OPtimization-INspired Explicable deep Network, dubbed OPINE …

To more efficiently address image compressed sensing (CS) problems, we present a novel
content-aware scalable network dubbed CASNet which collectively achieves adaptive …

The log-sum function as a penalty has always been drawing widespread attention in the
field of sparse problems. However, it brings a non-convex, non-smooth and non-Lipschitz …