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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.CV2024
Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric Learning
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +3
Foundation models have made incredible strides in achieving zero-shot or few-shot generalization, leveraging prompt engineering to mimic the problem-solving approach of human intel…
cs.CV2024★ 1 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…