Incorporating deep neural networks in image compressive sensing (CS) receives intensive
attentions in multimedia technology and applications recently. As deep network approaches …

Recently, deep learning-based compressive imaging (DCI) has surpassed conventional
compressive imaging in reconstruction quality and running speed. While multi-scale …

Inspired by certain optimization solvers, the deep unfolding network (DUN) usually inherits a
multi-phase structure for image compressive sensing (CS). However, in existing DUNs, the …

A novel visually secure image encryption algorithm is proposed by combining compressive
sensing and deep neural networks. To achieve a tradeoff between the visual quality and the …

Recently, deep neural network-based image compressed sensing methods have achieved
impressive success in reconstruction quality. However, these methods (1) have limitations in …

Compressed sensing (CS) is a technique in signal processing which reconstructs any given
signal at a rate less than that of Nyquist's' rate given that the signal is sparse and incoherent …

In this paper, we study how to reconstruct the original images from the given sensed
samples/measurements by proposing a so-called deep compressive image sensing …

Deep learning-based compressive sensing (DCS) has improved the single scale
compressive sensing (CS) with fast and high reconstruction quality. Researchers have …

Image compressed sensing technology, particularly algorithm unrolling networks, has
garnered significant attention in the field of compressed sensing due to their interpretability …

High-definition images covering entire large-scene construction sites are increasingly used
for monitoring management. However, the transmission of high-definition images is a huge …