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

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 …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

Image steganography aims to hide secret images into a container image, where the secret is
hidden from human vision and can be restored when necessary. Previous image …

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …

Global operations, such as global average pooling, are widely used in top-performance
image restorers. They aggregate global information from input features along entire spatial …

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

Recent years have witnessed great progress in image restoration thanks to the
advancements in modern deep neural networks eg Convolutional Neural Network and …

Digital image authenticity has promoted research on image forgery localization. Although
deep learning-based methods achieve remarkable progress, most of them usually suffer …