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 …

To provide users with more realistic visual experiences, videos are develo** in the trends
of Ultra High Definition (UHD), High Frame Rate (HFR), High Dynamic Range (HDR), Wide …

Many learning-based algorithms have been developed to solve the inverse problem of
coded aperture snapshot spectral imaging (CASSI). However, CNN-based methods show …

Image steganography aims to hide secret images into a container image, where the secret is
hidden from human vision and can be restored when necessary. Previous image …

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 …

Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …

Local and non-local attention-based methods have been well studied in various image
restoration tasks while leading to promising performance. However, most of the existing …

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …