Papers › Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
Yuhui Wu, Chen Pan, Guoqing Wang, Yang Yang, Jiwei Wei, Chongyi Li, Heng Tao Shen
Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Low-Light Image Enhancement | LOLv2 | LLFlow-L-SKF | Average PSNR | 28.45 | #6 of 12 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOLv2 | LLFlow-L-SKF | LPIPS | 0.112 | #6 of 12 | Archive leaderboard | report |
| Low-Light Image Enhancement | LOLv2 | LLFlow-L-SKF | SSIM | 0.905 | #6 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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