Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

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

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …

Transformer has recently gained considerable popularity in low-level vision tasks, including
image super-resolution (SR). These networks utilize self-attention along different …

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

Over the past few years, single image super-resolution (SR) has become a hotspot in the
remote sensing area, and numerous methods have made remarkable progress in this …

Transformer-based methods have shown impressive performance in low-level vision tasks,
such as image super-resolution. However, we find that these networks can only utilize a …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

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