FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement
This work provides a robust solution for enhancing low-light images, which is important for applications in photography and computer vision, though it appears incremental as it builds on existing fusion strategies.
The paper tackled the problem of low-light image enhancement by introducing FusionNet, a multi-model linear fusion framework that addresses challenges like parameter explosion and optimization instability, achieving first place in the CVPR2025 NTIRE Low Light Enhancement Challenge and outperforming state-of-the-art methods in quantitative and qualitative results.
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.