Image restoration is a critical task in low-level computer vision, aiming to restore high-quality
images from degraded inputs. Various models, such as convolutional neural networks …

Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good
performance but require powerful hardwares with enormous memory and computational …

Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from
its corresponding low-resolution (LR) input image with unknown degradations. Most of the …

Recently deep neural networks have achieved excellent performance on low-light raw video
enhancement. However they often come with high computational complexity and large …

Abstract Video Snapshot Compressive Imaging (SCI) aims to use a low-speed 2D camera to
capture high-speed scene as snapshot compressed measurements, followed by a …

Blind image super-resolution (SR) aims to recover high-resolution (HR) images from low-
resolution (LR) inputs hindered by unknown degradation. Existing blind SR methods exploit …

Binarized image super-resolution (SR) has attracted much research attention due to its
potential to drastically reduce parameters and operations. However, most binary SR works …

Image restoration poses a garners substantial interest due to the exponential surge in
demands for recovering high-quality images from diverse mobile camera devices, adverse …

Diffusion models (DMs) have been significantly developed and widely used in various
applications due to their excellent generative qualities. However, the expensive computation …

Advanced diffusion models (DMs) perform impressively in image super-resolution (SR), but
the high memory and computational costs hinder their deployment. Binarization, an ultra …