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Feng Zhang

4 papers hereh-index 398 citations4 works total

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

author position
  • first author2
  • middle author2

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

fields
  • cs.CV3
  • eess.IV1
same name
  • Feng Zhang — 34 papers, h 23
  • Feng Zhang — 17 papers, h 63
  • Feng Zhang — 13 papers, h 13
  • Feng Zhang — 10 papers, h 4
  • Feng Zhang — 8 papers, h 6
  • Feng Zhang — 7 papers, h 8

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
20212023
most citedUnsupervised Low-Light Image Enhancement via Histogram Equalization Prior

25 citations · 26 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2023

Towards General Low-Light Raw Noise Synthesis and Modeling

Feng Zhang, Bin Xu, Zhiqiang Li +4

Modeling and synthesizing low-light raw noise is a fundamental problem for computational photography and image processing applications. Although most recent works have adopted phys…

cs.CV2022

The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation

Leilei Cao, Zhuang Li, Bo Yan +4

The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames. Due to the requirement…

cs.CV2021★ 25 cited

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior

Feng Zhang, Yuanjie Shao, Yishi Sun +3

Deep learning-based methods for low-light image enhancement typically require enormous paired training data, which are impractical to capture in real-world scenarios. Recently, uns…

eess.IV2021★ 1 cited

Efficient Deep Image Denoising via Class Specific Convolution

Lu Xu, Jiawei Zhang, Xuanye Cheng +3

Deep neural networks have been widely used in image denoising during the past few years. Even though they achieve great success on this problem, they are computationally inefficien…

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