Abstract
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.
Abstract (translated)
当前用于恢复曝光不足图像的方法通常依赖于监督学习，需要成对的曝光不足和光照良好的图像数据。然而，在实际场景中收集这样的数据集往往是不切实际的。此外，这些方法还可能导致过度增强，从而扭曲了原本光照良好的区域。为了解决这些问题，我们提出了IGDNet（Illumination Guided Decomposition Network），这是一种零样本增强方法，仅基于单个测试图像进行操作，并且不需要引导先验或训练数据。IGDNet展示了强大的泛化能力和在恢复照明的同时有效抑制噪声的能力。该框架包括分解模块和去噪模块：前者通过密集连接网络将图像分离为光照和反射组件；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。通过对降采样生成的噪声对进行迭代细化以产生最终结果。在四个公开数据集上进行了广泛的实验，结果显示IGDNet在复杂照明条件下显著提高了视觉质量。基于PSNR（20.41dB）和SSIM（0.860）等指标的定量结果表明，它优于现有的14种最先进的无监督方法。代码将很快发布。
URL
https://arxiv.org/abs/2507.02445
