Revisiting Lightweight Low-Light Image Enhancement: From a YUV Color Space Perspective

TL;DR

This paper introduces a YUV color space-based approach for lightweight low-light image enhancement, leveraging frequency analysis to improve quality while maintaining model efficiency.

Contribution

It proposes a novel YUV-based paradigm with specialized modules for channel restoration, addressing channel-specific degradation patterns overlooked by prior methods.

Findings

Achieves state-of-the-art performance on multiple benchmarks.

Reduces model parameters significantly compared to existing methods.

Demonstrates superior visual quality in low-light image enhancement.

Abstract

In the current era of mobile internet, Lightweight Low-Light Image Enhancement (L3IE) is critical for mobile devices, which faces a persistent trade-off between visual quality and model compactness. While recent methods employ disentangling strategies to simplify lightweight architectural design, such as Retinex theory and YUV color space transformations, their performance is fundamentally limited by overlooking channel-specific degradation patterns and cross-channel interactions. To address this gap, we perform a frequency-domain analysis that confirms the superiority of the YUV color space for L3IE. We identify a key insight: the Y channel primarily loses low-frequency content, while the UV channels are corrupted by high-frequency noise. Leveraging this finding, we propose a novel YUV-based paradigm that strategically restores channels using a Dual-Stream Global-Local Attention module for the Y channel, a Y-guided Local-Aware Frequency Attention module for the UV channels, and a Guided Interaction module for final feature fusion. Extensive experiments validate that our model establishes a new state-of-the-art on multiple benchmarks, delivering superior visual quality with a significantly lower parameter count.