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Cong Wang

4 papers hereh-index 11520 citations24 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • eess.IV1
same name
  • Cong Wang — 106 papers, h 5
  • Cong Wang — 19 papers, h 6
  • Cong Wang — 14 papers, h 4
  • Cong Wang — 12 papers, h 29
  • Cong Wang — 9 papers, h 5
  • Cong Wang — 9 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedJoint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network

7 citations · 7 across the 2 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 5 cited

Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration

Liyan Wang, Qinyu Yang, Cong Wang +3

Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper de…

cs.CV2022

Online-updated High-order Collaborative Networks for Single Image Deraining

Cong Wang, Jinshan Pan, Xiao-Ming Wu

Single image deraining is an important and challenging task for some downstream artificial intelligence applications such as video surveillance and self-driving systems. Most of th…

cs.CV2020★ 7 cited

Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network

Cong Wang, Yutong Wu, Zhixun Su +1

In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions. I…

cs.CV2020

DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal

Cong Wang, Xiaoying Xing, Zhixun Su +1

Rain removal is an important but challenging computer vision task as rain streaks can severely degrade the visibility of images that may make other visions or multimedia tasks fail…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.