用轻量级网络和分布归一化，高效提升暗光图像质量。
Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net
- 先用固定算法预处理，再用深度可分离卷积的U-Net做残差修复
- 参数量远低于现有方法，仍获CVPR 2026挑战赛第3名
- 适合移动端或实时暗光增强场景
我们提出一种轻量级两阶段暗光图像增强框架，在参数量显著减少的情况下，仍达到具有竞争力的感知质量。方法结合冻结的算法预处理与完全由深度可分离卷积构建的紧凑U-Net。预处理通过提供互补的亮度修正视图，对输入分布进行归一化，使可训练网络专注于残差色彩校正。该方法在CVPR 2026 NTIRE高效暗光图像增强挑战赛中获得第3名。我们还提供了扩展基准测试与消融实验，验证了方法的通用有效性。
原文摘要 · Abstract (English)
We present a lightweight two-stage framework for low-light image enhancement (LLIE) that achieves competitive perceptual quality with significantly fewer parameters than existing methods. Our approach combines frozen algorithm-based preprocessing with a compact U-Net built entirely from depthwise-separable convolutions. The preprocessing normalizes the input distribution by providing complementary brightness-corrected views, enabling the trainable network to focus on residual color correction. Our method achieved 3rd place in the CVPR 2026 NTIRE Efficient Low-Light Image Enhancement Challenge. We further provide extended benchmarks and ablations to demonstrate the general effectiveness of our methods.
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