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

TL;DR

This paper introduces a lightweight, two-stage low-light image enhancement framework combining distribution-normalizing preprocessing with a depthwise U-Net, achieving high performance with fewer parameters.

Contribution

The novel combination of distribution-normalizing preprocessing and a depthwise U-Net results in a compact, effective LLIE method with competitive perceptual quality.

Findings

Achieved 3rd place in CVPR 2026 NTIRE Challenge.

Demonstrated effectiveness through extended benchmarks and ablations.

Significantly fewer parameters than existing methods.

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.