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Chenglin Li

28 papers hereh-index 252k citations162 works total

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

author position
  • middle author28

Across the 28 of 28 papers where every author was matched, so the position is known.

fields
  • cs.CV13
  • cs.LG8
  • eess.IV3
  • cs.IT2
  • cs.DC1
  • cs.MM1
same name
  • Chenglin Li — 10 papers, h 4
  • Chenglin Li — 9 papers, h 7
  • Chenglin Li — 8 papers, h 1
  • Chenglin Li — 3 papers, h 1
  • Chenglin Li — 2 papers, h 1
  • Chenglin Li — 2 papers, h 1

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
20192026
most citedHybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks

27 citations · 58 across the 28 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks

Wenqiang Xu, Wenrui Dai, Duoduo Xue +4

Due to limitations in acquisition equipment, noise perturbations often corrupt 3-D point clouds, hindering down-stream tasks such as surface reconstruction, rendering, and further…

cs.CV2024

Point Cloud Resampling with Learnable Heat Diffusion

Wenqiang Xu, Wenrui Dai, Duoduo Xue +4

Generative diffusion models have shown empirical successes in point cloud resampling, generating a denser and more uniform distribution of points from sparse or noisy 3D point clou…

cs.CV2024

Image Compression for Machine and Human Vision with Spatial-Frequency Adaptation

Han Li, Shaohui Li, Shuangrui Ding +5

Image compression for machine and human vision (ICMH) has gained increasing attention in recent years. Existing ICMH methods are limited by high training and storage overheads due…

cs.CV2024

Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance

Xinyu Peng, Ziyang Zheng, Wenrui Dai +4

Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems. In this paper, we reveal that rece…

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