Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

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 …

Recent years have seen significant advancements in image restoration, largely attributed to
the development of modern deep neural networks, such as CNNs and Transformers …

Recently, diffusion models have been used to solve various inverse problems in an
unsupervised manner with appropriate modifications to the sampling process. However, the …

Abstract Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and
interpretable method for solving various inverse problems by utilizing any off-the-shelf …

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

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) …

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 …

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 …

Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …