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

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 …

Convolutional Neural Networks (CNNs) dominate image processing but suffer from local
inductive bias, which is addressed by the transformer framework with its inherent ability to …

Abstract Short-Term Single-Object (STSO) tracking using Hyperspectral Videos (HSVs),
which has become a hotspot recently, is a challenging task. Hyperspectral Object Tracking …

Deep unrolling architectures have revitalized compressive sensing (CS) by seamlessly
blending deep neural networks with traditional optimization-based reconstruction …

By mapping iterative optimization algorithms into neural networks (NNs), deep unfolding
networks (DUNs) exhibit well-defined and interpretable structures and achieve remarkable …

Deep unfolding networks (DUNs) renowned for their interpretability and superior
performance have invigorated the realm of compressive sensing (CS). Nonetheless existing …

In the domain of compressive sensing (CS) deep unfolding networks (DUNs) have garnered
attention for their good performance and certain degree of interpretability rooted in CS …

Single-pixel imaging (SPI) is a potential computational imaging technique which produces
image by solving an ill-posed reconstruction problem from few measurements captured by a …

Abstract Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent
pursuit holding substantial promise for the enhancement of Compressive Sensing (CS) …