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arXiv · 2604.11071

Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net

Shimon Murai·Teppei Kurita·Ryuta Satoh·Yusuke Moriuchi
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Abstract

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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Shimon Murai, Teppei Kurita, Ryuta Satoh, Yusuke Moriuchi. 2026-04-13. Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net. https://arxiv.org/abs/2604.11071

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