IGDNet：基于光照引导与去噪的零样本鲁棒欠曝光图像增强
IGDNet: Zero-Shot Robust Underexposed Image Enhancement via Illumination-Guided and Denoising
AI总结：
本文提出零样本欠曝光图像增强方法IGDNet，通过光照分解与光照引导像素自适应校正及迭代去噪，在无需训练数据的情况下实现鲁棒增强，性能优于14种无监督方法。
摘要：
当前恢复欠曝光图像的方法通常依赖成对的欠曝光与正常光照图像进行监督学习。然而，在真实场景中收集此类数据集往往不切实际。此外，这些方法可能导致过度增强，使原本光照良好的区域发生失真。为解决这些问题，我们提出IGDNet，一种仅对单张测试图像进行操作的零样本增强方法，无需引导先验或训练数据。IGDNet展现出强大的泛化能力，在恢复光照的同时有效抑制噪声。该框架包含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离为光照分量和反射分量，后者采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，并迭代细化以产生最终结果。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升了视觉质量。PSNR（20.41dB）和SSIM（0.860dB）等指标的定量结果表明，其性能优于14种最先进的无监督方法。代码即将发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
