Over the last years, advances in deep learning have brought huge developments to the
studying of super-resolution reconstruction. However, most super-resolution methods only …

Recently, Transformer-based methods have demonstrated satisfactory results on lightweight
Image Super-Resolution. However, most of them limit the computational range of …

Burst image super-resolution is an ill-posed problem that aims to restore a high-resolution
(HR) image from a sequence of low-resolution (LR) burst images. To restore a photo …

JPEG images are usually corrupted by various undesirable compression artifacts resulted
from block-wise coarse quantization on discrete cosine transform coefficients. In recent …

This paper proposes a coding method for compressing a phase-only hologram video
(PoHV), which can be directly displayed in a commercial phase-only spatial light modulator …

Image restoration based on the Deep Convolutional Neural Network (CNN) based image
restoration has demonstrated promising results in many sub-tasks, such as image super …

Most CNN-based super-resolution networks require a large number of samples for model
training, which may cause overfitting when trained on a specific set, and the internal self …

Single image super-resolution (SISR) has achieved great success in recent years due to the
representation ability of large and deep models. However, these models usually have a …

This paper proposes a novel edge synthesis block as the basic building unit of model
architectures for single image super resolution tasks. The edge synthesis block consists of …