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Jun Xu

9 papers hereh-index 122k citations23 works total

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

author position
  • first author6
  • middle author1
  • last author1

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV9
same name
  • Jun Xu — 104 papers, h 27
  • Jun Xu — 21 papers, h 16
  • Jun Xu — 19 papers, h 26
  • Jun Xu — 17 papers, h 7
  • Jun Xu — 16 papers, h 9
  • Jun Xu — 14 papers, h 9

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
20172023
most citedReal-world Noisy Image Denoising: A New Benchmark

173 citations · 392 across the 9 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CV2018★ 18 cited

A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising

Jun Xu, Lei Zhang, David Zhang

Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more…

cs.CV2018★ 2 cited

Scaled Simplex Representation for Subspace Clustering

Jun Xu, Mengyang Yu, Ling Shao +4

The self-expressive property of data points, i.e., each data point can be linearly represented by the other data points in the same subspace, has proven effective in leading subspa…

cs.CV2018★ 5 cited

Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?

Jun Xu, Wangpeng An, Lei Zhang +1

The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categori…

cs.CV2018★ 173 cited

Real-world Noisy Image Denoising: A New Benchmark

Jun Xu, Hui Li, Zhetong Liang +2

Most of previous image denoising methods focus on additive white Gaussian noise (AWGN). However,the real-world noisy image denoising problem with the advancing of the computer visi…

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