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.
@article{arxiv.2604.11071,
title = {Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net},
author = {Shimon Murai and Teppei Kurita and Ryuta Satoh and Yusuke Moriuchi},
journal= {arXiv preprint arXiv:2604.11071},
year = {2026}
}
Comments
Technical report for the NTIRE 2026 Efficient Low-Light Image Enhancement Challenge (CVPR 2026 Workshops), 3rd place solution