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

2026-04-13

AI Summary

This work addresses the challenge of high model complexity and excessive parameter count in low-light image enhancement by proposing a lightweight two-stage enhancement framework. The method integrates a frozen distribution normalization preprocessing module—which provides luminance correction priors—with a compact U-Net architecture built entirely from depthwise separable convolutions to perform residual color correction. This design significantly reduces model parameters while preserving excellent perceptual quality. The proposed approach secured fourth place in the CVPR 2026 NTIRE Challenge on Efficient Low-Light Image Enhancement and demonstrates strong effectiveness and generalizability through comprehensive benchmarking and ablation studies.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Web Mining and Content Analysis: Large pretrained models with web data

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 4th 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.

Problem

• low-light image enhancement
• lightweight
• efficient
• perceptual quality
• parameter efficiency

Innovation

• distribution-normalizing preprocessing
• depthwise-separable convolutions
• lightweight U-Net
• low-light image enhancement
• two-stage framework