activity
20222024
most citedEDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution

8 citations · 16 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

The Third Monocular Depth Estimation Challenge

Jaime Spencer, Fabio Tosi, Matteo Poggi +38

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-…

cs.CV2024

Exploiting Self-Supervised Constraints in Image Super-Resolution

Gang Wu, Junjun Jiang, Kui Jiang +1

Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-…

cs.CV20231 cited

Dynamic Association Learning of Self-Attention and Convolution in Image Restoration

Kui Jiang, Xuemei Jia, Wenxin Huang +3

CNNs and Self attention have achieved great success in multimedia applications for dynamic association learning of self-attention and convolution in image restoration. However, CNN…

eess.IV20238 cited

EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution

Yi Xiao, Qiangqiang Yuan, Kui Jiang +3

Recently, convolutional networks have achieved remarkable development in remote sensing image Super-Resoltuion (SR) by minimizing the regression objectives, e.g., MSE loss. However…

cs.CV2023

From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement

Wanyu Wu, Wei Wang, Zheng Wang +2

Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. How…

cs.CV20226 cited

Magic ELF: Image Deraining Meets Association Learning and Transformer

Kui Jiang, Zhongyuan Wang, Chen Chen +3

Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications. However, little effort has been made to effectively and efficiently harmo…