πŸ“„
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model paper by Xunpeng Yi, Han Xu, H. Zhang, Linfeng Tang, Jiayi Ma
⭐ 59.4
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Paper

Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model

by Xunpeng Yi, Han Xu, H. Zhang, Linfeng Tang, Jiayi Ma 2308.13164
Free2AITools Nexus Index
59.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 89
P: Popularity 67
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. We aim to integrate the advantages of the physical model and the generative network. Furthermore, we hope to supplement and even deduce the information missing in the low-light image through the generative network. Therefore, Diff-Retinex formulates the lowlight image enhancement problem into Retinex decompo...

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Semantic Scholar 216 Citations
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Registry ID 2308.13164
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2308_13164,
  author = {Xunpeng Yi, Han Xu, H. Zhang, Linfeng Tang, Jiayi Ma},
  title = {Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2308.13164}},
  note = {Accessed via Free2AITools.}
}
APA Style
Xunpeng Yi, Han Xu, H. Zhang, Linfeng Tang, Jiayi Ma. (2026). Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model [Paper]. Free2AITools. https://arxiv.org/abs/2308.13164

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 89
Popularity (P) 67
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model: Authority (A:89), Popularity (P:67), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. We aim to integrate the advantages of the physical model and the generative network. Furthermore, we hope to supplement and even deduce the information missing in the low-light image through the generative network. Therefore, Diff-Retinex formulates the lowlight image enhancement problem into Retinex decompo..."

❝ Cite Node 

@article{Yi2026Diff-Retinex:,
  title={Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model},
  author={Xunpeng Yi and Han Xu and H. Zhang and Linfeng Tang and Jiayi Ma},
  journal={arXiv preprint arXiv:2308.13164},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Xunpeng Yi Han Xu H. Zhang Linfeng Tang Jiayi Ma

πŸ”— Full Paper

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πŸ“Š Research Signals

πŸ“ˆ216CitationsSemantic Scholar
πŸ›οΈ89AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈtext generationField

🏷️ Research Topics

image generation
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πŸ†” Identity & Source

id
2308.13164
slug
2308.13164
source
semantic_scholar
author
Xunpeng Yi, Han Xu, H. Zhang, Linfeng Tang, Jiayi Ma
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
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citations
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