NTIRE 2024 challenge on blind enhancement of compressed image: Methods and results
This paper reviews the Challenge on Blind Enhancement of Compressed Image at NTIRE
2024 which aims at enhancing the quality of JPEG images which are compressed with …
2024 which aims at enhancing the quality of JPEG images which are compressed with …
Deep learning on image denoising: An overview
Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …
However, there are substantial differences in the various types of deep learning methods …
Efficient long-range attention network for image super-resolution
Recently, transformer-based methods have demonstrated impressive results in various
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …
Multi-stage image denoising with the wavelet transform
Deep convolutional neural networks (CNNs) are used for image denoising via automatically
mining accurate structure information. However, most of existing CNNs depend on enlarging …
mining accurate structure information. However, most of existing CNNs depend on enlarging …
Swinir: Image restoration using swin transformer
Image restoration is a long-standing low-level vision problem that aims to restore high-
quality images from low-quality images (eg, downscaled, noisy and compressed images) …
quality images from low-quality images (eg, downscaled, noisy and compressed images) …
Deep generalized unfolding networks for image restoration
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 …
most DNN methods are designed as a black box, lacking transparency and interpretability …
Learning enriched features for fast image restoration and enhancement
Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …
image content. Numerous applications demand effective image restoration, eg …
All-in-one image restoration for unknown corruption
In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …
an all-in-one method that could recover images from a variety of unknown corruption types …
Transformer for single image super-resolution
Single image super-resolution (SISR) has witnessed great strides with the development of
deep learning. However, most existing studies focus on building more complex networks …
deep learning. However, most existing studies focus on building more complex networks …
Srformer: Permuted self-attention for single image super-resolution
Previous works have shown that increasing the window size for Transformer-based image
super-resolution models (eg, SwinIR) can significantly improve the model performance but …
super-resolution models (eg, SwinIR) can significantly improve the model performance but …