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 …

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 …

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

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

3D whole-body human mesh recovery aims to reconstruct the 3D human body, face, and
hands from a single image. Although powerful deep learning models have achieved …

Blind image super-resolution (SR) aims to recover a high-resolution (HR) image from its low-
resolution (LR) counterpart under the assumption of unknown degradations. Many existing …

Binary neural network (BNN) is an effective approach to reduce the memory usage and the
computational complexity of full-precision convolutional neural networks (CNNs), which has …