English(EN) DARD: Zero-Shot Degradation-Aware Retinex-Guided Diffusion for Low-Light Image Enhancement

新的DARD框架利用Retinex先验增强低光图像

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了DARD，一种新颖的用于低光图像增强的零样本框架，该框架利用Retinex模型进行结构引导。该方法分解退化的输入图像以提取物理先验，然后将其集成到扩散过程中。DARD旨在提高增强图像的结构一致性和颜色准确性，其性能优于现有的零样本基线，并在下游语义分割任务的mIoU方面显示出显著的相对改进。 AI

影响这项研究可能为包括语义分割在内的各种计算机视觉任务在低光条件下的图像质量带来改进。

排序理由详细介绍一种新图像增强方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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本文如何被排名

Signal score: 20 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍一种新图像增强方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Wenjie Cai, Yuezhe Yang, Jianyang Xia, Xingbo Dong, Zhe Jin · 2026-09-01 04:00

DARD：零样本退化感知Retinex引导扩散用于低光图像增强

arXiv:2608.29243v1 Announce Type: new Abstract: Existing diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paired supervision or lack reliable scene constraints in zero-shot settings, often lea…

本文如何被排名

报道来源 [1]

DARD：零样本退化感知Retinex引导扩散用于低光图像增强

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