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most citedAGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement

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cs.CV2026

MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimization

Haitao Zhang, Yingying Wang, Jiaxiang Wang +5

Medical Anomaly Detection (MedAD) presents a significant opportunity to enhance diagnostic accuracy using Large Multimodal Models (LMMs) to interpret and answer questions based on…

cs.CV2025

FRN: Fractal-Based Recursive Spectral Reconstruction Network

Ge Meng, Zhongnan Cai, Ruizhe Chen +4

Generating hyperspectral images (HSIs) from RGB images through spectral reconstruction can significantly reduce the cost of HSI acquisition. In this paper, we propose a Fractal-Bas…

cs.CV2025

Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables

Zhongnan Cai, Yingying Wang, Hui Zheng +10

Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computat…

cs.CV2024

Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors

Yunlong Lin, Zhenqi Fu, Kairun Wen +7

Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep ne…

cs.CV20241 cited

AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement

Yunlong Lin, Tian Ye, Sixiang Chen +6

Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications. The limit…