标题： IGDNet：通过光照引导和去噪实现零样本鲁棒的欠曝光图像增强 显示英文标题

标题： IGDNet: Zero-Shot Robust Underexposed Image Enhancement via Illumination-Guided and Denoising

摘要： 当前用于恢复曝光不足图像的方法通常依赖于成对的曝光不足和光照良好的图像进行监督学习。 然而，在现实场景中收集这样的数据集通常不切实际。 此外，这些方法可能导致过度增强，扭曲光照良好的区域。 为了解决这些问题，我们提出了IGDNet，这是一种零样本增强方法，仅在单个测试图像上运行，无需引导先验或训练数据。 IGDNet表现出强大的泛化能力，并在恢复光照的同时有效抑制噪声。 该框架包括一个分解模块和一个去噪模块。 前者通过密集连接网络将图像分为光照和反射成分，而后者则使用光照引导的像素自适应校正方法来增强非均匀光照区域。 通过下采样生成噪声对，并进行迭代优化以产生最终结果。 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高了视觉质量。 在PSNR（20.41dB）和SSIM（0.860dB）等指标上的定量结果表明，它优于14种最先进的无监督方法。 代码即将发布。 显示英文摘要

摘要： 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.