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researcher

W. Dong

6 papers hereh-index 4211.6k citations178 works total

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

author position
  • first author1
  • middle author5

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • eess.IV4
  • cs.CV2
same name
  • W. Dong — 6 papers, h 12
  • W. Dong — 5 papers, h 22
  • W. Dong — 3 papers, h 26
  • W. Dong — 2 papers
  • W. Dong — 2 papers
  • W. Dong — 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
20182021
most citedConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning

28 citations · 56 across the 4 of their papers we have counts for

collaborators
Showing eess.IVShow all

4 papers · 1 filter

eess.IV2021

Searching Efficient Model-guided Deep Network for Image Denoising

Qian Ning, Weisheng Dong, Xin Li +3

Neural architecture search (NAS) has recently reshaped our understanding on various vision tasks. Similar to the success of NAS in high-level vision tasks, it is possible to find a…

eess.IV2021★ 7 cited

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

Tao Huang, Weisheng Dong, Xin Yuan +2

In coded aperture snapshot spectral imaging (CASSI) system, the real-world hyperspectral image (HSI) can be reconstructed from the captured compressive image in a snapshot. Model-b…

eess.IV2020★ 21 cited

MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment

Hancheng Zhu, Leida Li, Jinjian Wu +2

Recently, increasing interest has been drawn in exploiting deep convolutional neural networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the notable suc…

eess.IV2018

Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding

Fangfang Wu, Weisheng Dong, Guangming Shi +1

State-of-the-art approaches toward image restoration can be classified into model-based and learning-based. The former - best represented by sparse coding techniques - strive to ex…

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