用线性融合提升暗光图像增强，效果更稳更强。
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement
- 并行处理多模型多色彩空间特征，利用希尔伯特空间理论保证融合稳定。
- 在CVPR2025挑战赛中排名第一，多个数据集上超越现有方法。
- 适合需要高鲁棒性暗光增强的工程应用或算法研究者。
深度神经网络推动了低光照图像增强（LLIE）的显著进展，各类架构（如CNNs和Transformers）与色彩空间（如sRGB、HSV、HVI）带来了优异结果。近期工作尝试融合不同范式的互补优势，以应对多样退化场景。然而，现有融合策略受限于参数爆炸、优化不稳定和特征错位等问题，制约性能提升。为此，本文提出FusionNet，一种新型多模型线性融合框架，在多个色彩空间中并行捕捉全局与局部特征。通过基于希尔伯特空间理论的线性融合策略，有效防止网络坍塌，降低训练成本。该方法在CVPR2025 NTIRE低光照增强挑战赛中获得第一名。在合成与真实世界基准数据集上的大量实验表明，所提方法在定量与定性指标上均显著优于现有先进方法，展现出对多种低光条件下的强鲁棒性增强能力。
原文摘要 · Abstract (English)
The advent of Deep Neural Networks (DNNs) has driven remarkable progress in low-light image enhancement (LLIE), with diverse architectures (e.g., CNNs and Transformers) and color spaces (e.g., sRGB, HSV, HVI) yielding impressive results. Recent efforts have sought to leverage the complementary strengths of these paradigms, offering promising solutions to enhance performance across varying degradation scenarios. However, existing fusion strategies are hindered by challenges such as parameter explosion, optimization instability, and feature misalignment, limiting further improvements. To overcome these issues, we introduce FusionNet, a novel multi-model linear fusion framework that operates in parallel to effectively capture global and local features across diverse color spaces. By incorporating a linear fusion strategy underpinned by Hilbert space theoretical guarantees, FusionNet mitigates network collapse and reduces excessive training costs. Our method achieved 1st place in the CVPR2025 NTIRE Low Light Enhancement Challenge. Extensive experiments conducted on synthetic and real-world benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art methods in terms of both quantitative and qualitative results, delivering robust enhancement under diverse low-light conditions.
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