用双通道傅里叶变换提升暗光图像亮度与色彩自然度
LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration
- 双色空间（LAB/YUV）分离亮度与颜色，提升处理精度
- 引入Transformer与傅里叶模块，实现高效全局建模与均匀亮度调节
- 轻量化设计，实现在多个数据集上超越现有方法
我们提出LTCF-Net，一种用于暗光图像增强的新网络架构。与基于Retinex的方法不同，本方法利用LAB和YUV两个颜色空间，有效分离并处理颜色信息，借助图像中亮度与色度成分的分离特性。模型融合Transformer结构以全面理解图像内容，同时保持计算效率。为动态平衡输出图像亮度，引入傅里叶变换模块，在频域调整亮度通道，实现区域间亮度均匀分布，并消除背景噪声，从而提升视觉质量。通过结合这些创新组件，LTCF-Net在保持模型轻量的同时显著改善暗光图像质量。实验结果表明，该方法在多个评估指标和数据集上优于当前最优方法，实现更自然的颜色还原与均衡的亮度分布。
原文摘要 · Abstract (English)
We introduce LTCF-Net, a novel network architecture designed for enhancing low-light images. Unlike Retinex-based methods, our approach utilizes two color spaces - LAB and YUV - to efficiently separate and process color information, by leveraging the separation of luminance from chromatic components in color images. In addition, our model incorporates the Transformer architecture to comprehensively understand image content while maintaining computational efficiency. To dynamically balance the brightness in output images, we also introduce a Fourier transform module that adjusts the luminance channel in the frequency domain. This mechanism could uniformly balance brightness across different regions while eliminating background noises, and thereby enhancing visual quality. By combining these innovative components, LTCF-Net effectively improves low-light image quality while keeping the model lightweight. Experimental results demonstrate that our method outperforms current state-of-the-art approaches across multiple evaluation metrics and datasets, achieving more natural color restoration and a balanced brightness distribution.
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