无需训练数据，单图修复暗光图像，还能去噪保真。
IGDNet: Zero-Shot Robust Underexposed Image Enhancement via Illumination-Guided and Denoising
- 仅凭一张测试图，通过光照分解与自适应修正实现增强。
- 在4个公开数据集上达到20.41dB的PSNR和0.860dB的SSIM。
- 适合无配对数据、复杂光照场景下的图像恢复任务。
现有暗光图像恢复方法通常依赖成对的低光与正常光照图像进行监督学习，但真实场景中收集此类数据极为困难。此外，这些方法常导致过增强，破坏正常光照区域。为此，我们提出IGDNet，一种零样本图像增强方法，仅需单张测试图像，无需引导先验或训练数据。IGDNet具备强泛化能力，能有效抑制噪声并恢复光照。其框架包含分解模块与去噪模块：前者通过密集连接网络将图像分解为光照与反射分量；后者利用光照引导的像素自适应校正方法增强非均匀光照区域。通过下采样生成噪声对，并迭代优化得到最终结果。在四个公开数据集上的大量实验表明，IGDNet在复杂光照条件下显著提升视觉质量。定量指标显示，其在PSNR（20.41dB）和SSIM（0.860dB）上优于14种前沿无监督方法。代码即将开源。
原文摘要 · Abstract (English)
Current methods for restoring underexposed images typically rely on supervised learning with paired underexposed and well-illuminated images. However, collecting such datasets is often impractical in real-world scenarios. Moreover, these methods can lead to over-enhancement, distorting well-illuminated regions. To address these issues, we propose IGDNet, a Zero-Shot enhancement method that operates solely on a single test image, without requiring guiding priors or training data. IGDNet exhibits strong generalization ability and effectively suppresses noise while restoring illumination. The framework comprises a decomposition module and a denoising module. The former separates the image into illumination and reflection components via a dense connection network, while the latter enhances non-uniformly illuminated regions using an illumination-guided pixel adaptive correction method. A noise pair is generated through downsampling and refined iteratively to produce the final result. Extensive experiments on four public datasets demonstrate that IGDNet significantly improves visual quality under complex lighting conditions. Quantitative results on metrics like PSNR (20.41dB) and SSIM (0.860dB) show that it outperforms 14 state-of-the-art unsupervised methods. The code will be released soon.
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