通过多尺度注意力与傅里叶变换相结合的低光照图像增强
Low-light Image Enhancement via Multi-scale Attention combined with Fourier Transform
- East China University of Science and Technology（华东理工大学）
- Tongji University（同济大学）
- Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究低光照图像增强问题，提出基于多尺度注意力与傅里叶变换结合的MSFT网络，通过独特架构、信息融合及注意力机制有效增强图像，在多个数据集实验中显著优于现有方法。
AI中文摘要：
低光照图像增强旨在改善不同低光照环境下的图像质量和清晰度。现有基于深度学习的方法难以准确捕捉真实世界光照并恢复纹理细节。为此提出一种用于低光照图像增强的监督频域深度学习网络——多尺度注意力与傅里叶变换相结合（MSFT）。它采用U形单阶段架构，通过多尺度注意力引入低光照图像引导，在自建模块中融合先验通道幅度信息并进行多尺度引导，在解码阶段引入多形状协同注意力和轻量级网络。在多个数据集上的实验表明，MSFT显著优于现有方法。
英文摘要：
Low-light image enhancement (LLIE) aims to improve image quality and clarity in diverse and demanding low-illumination environments. However, existing deep learning-based LLIE methods struggle to accurately capture real-world illumination and restore texture details, largely because their algorithmic strengths remain underutilized. To address these issues, we present a supervised frequency domain deep learning network for LLIE, named multi-scale attention combined with the Fourier transform (MSFT) which adopts a U-shaped, one-stage architecture that infuses guidance from low-light images into the network by channeling it through multi-scale attention. We further fuse the amplitude information from priori channels with that of the low-light image in MSFT's self-created module, and carry out multi-scale guidance along with the network. Subsequently, to better enhance the faint feature, such as fine content and textures, and to better fuse global context confidence in the decoding stage, we separately introduce a multi-shape synergistic attention and a lightweight network that effectively integrate information in high-dimensional space to embed into the superlative feature space channel containing rich texture information. Extensive experiments conducted on LOL, SID, SMID, and SDSD datasets demonstrate that MSFT significantly outperforms state-of-the-art competitors. For example, compared with Retinexformer, our method achieves a peak signal-to-noise ratio of up to 41.76 decibels on the SDSD-outdoor dataset with an increase of 11.92 decibels and a structural similarity index of 0.988 with a 13.80% improvement.
