In this paper, we present Uformer, an effective and efficient Transformer-based architecture
for image restoration, in which we build a hierarchical encoder-decoder network using the …

Recently, Transformer architecture has been introduced into image restoration to replace
convolution neural network (CNN) with surprising results. Considering the high …

While Transformer has achieved remarkable performance in various high-level vision tasks,
it is still challenging to exploit the full potential of Transformer in image restoration. The crux …

Since convolutional neural networks (CNNs) perform well at learning generalizable image
priors from large-scale data, these models have been extensively applied to image …

Recently, Transformer-based image restoration networks have achieved promising
improvements over convolutional neural networks due to parameter-independent global …

Deep learning methods have witnessed the great progress in image restoration with specific
metrics (eg, PSNR, SSIM). However, the perceptual quality of the restored image is relatively …

Vision Transformers have shown promising performance in image restoration, which usually
conduct window-or channel-based attention to avoid intensive computations. Although the …

In this paper, we study design of deep neural networks for tasks of image restoration. We
propose a novel style of residual connections dubbed" dual residual connection", which …

Recent works attempt to integrate the non-local operation with CNNs or Transformer,
achieving remarkable performance in image restoration tasks. The global similarity …

Transformer-based approaches have achieved promising performance in image restoration
tasks given their ability to model long-range dependencies which is crucial for recovering …