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Xi Cheng

5 papers hereh-index 10436 citations18 works total

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

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV1
same name
  • Xi Cheng — 4 papers, h 3
  • Xi Cheng — 3 papers, h 14
  • Xi Cheng — 3 papers
  • Xi Cheng — 2 papers, h 7
  • Xi Cheng — 2 papers, h 3
  • Xi Cheng — 2 papers

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

activity
20182020
most citedSESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks

11 citations · 17 across the 3 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2020★ 3 cited

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Shanxin Yuan, Radu Timofte, Ales Leonardis +43

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demo…

cs.CV2019

Multi-scale Dynamic Feature Encoding Network for Image Demoireing

Xi Cheng, Zhenyong Fu, Jian Yang

The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos. Digital sensors, however, suffer from producing Moire when photo…

cs.CV2018

Triple Attention Mixed Link Network for Single Image Super Resolution

Xi Cheng, Xiang Li, Jian Yang

Single image super resolution is of great importance as a low-level computer vision task. Recent approaches with deep convolutional neural networks have achieved im-pressive perfor…

cs.CV2018★ 11 cited

SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks

Xi Cheng, Xiang Li, Ying Tai +1

Single image super resolution is a very important computer vision task, with a wide range of applications. In recent years, the depth of the super-resolution model has been constan…

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