Summary of Efficient Diffusion As Low Light Enhancer, by Guanzhou Lan et al.
Efficient Diffusion as Low Light Enhancer
by Guanzhou Lan, Qianli Ma, Yuqi Yang, Zhigang Wang, Dong Wang, Xuelong Li, Bin Zhao
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the computational challenge in diffusion-based Low-Light Image Enhancement (LLIE), a trade-off between performance and efficiency. Current acceleration methods often lead to significant performance degradation. The authors identify two primary factors: fitting errors and the inference gap. They propose a Reflectance-Aware Trajectory Refinement (RATR) module, which refines the teacher trajectory using reflectance information. This is integrated into ReDDiT, an efficient distillation framework for LLIE. ReDDiT achieves comparable performance to previous methods with reduced steps and establishes new state-of-the-art results on 10 benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers improve low-light image quality without slowing down. Right now, computers struggle to balance how well they do this job with how fast they can do it. The authors found two main reasons why computers have trouble: mistakes in understanding the image and not using all available information. They developed a new way to fix these problems called ReDDiT, which makes computer processing faster and better than before. This new method works on 10 different types of low-light images and performs just as well or even better than other methods that take longer. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Inference
