Abstract
Current exposure correction methods have three challenges, labor-intensive paired data annotation, limited generalizability, and performance degradation in low-level computer vision tasks. In this work, we introduce an innovative Unsupervised Exposure Correction (UEC) method that eliminates the need for manual annotations, offers improved generalizability, and enhances performance in low-level downstream tasks. Our model is trained using freely available paired data from an emulated Image Signal Processing (ISP) pipeline. This approach does not need expensive manual annotations, thereby minimizing individual style biases from the annotation and consequently improving its generalizability. Furthermore, we present a large-scale Radiometry Correction Dataset, specifically designed to emphasize exposure variations, to facilitate unsupervised learning. In addition, we develop a transformation function that preserves image details and outperforms state-of-the-art supervised methods [12], while utilizing only 0.01% of their parameters. Our work further investigates the broader impact of exposure correction on downstream tasks, including edge detection, demonstrating its effectiveness in mitigating the adverse effects of poor exposure on low-level features. The source code and dataset are publicly available at this https URL.
Abstract (translated)
目前的曝光校正方法面临着三个主要挑战：劳动密集型配对数据标注、有限的泛化能力和在低级计算机视觉任务中的性能下降。在这项工作中，我们引入了一种创新的无监督曝光校正（UEC）方法，该方法消除了手动注释的需求，提供了更好的泛化能力，并提高了低级下游任务的性能。我们的模型使用来自模拟图像信号处理（ISP）管道的免费配对数据进行训练。这种做法不需要昂贵的手动标注，从而减少了个人风格偏见的影响，进而提高其泛化性。 此外，我们还提出了一套大规模的辐射校正数据集，专门设计用于强调曝光变化，以促进无监督学习。另外，我们开发了一个转换函数，该函数保留图像细节，并且在性能上超越了最先进的监督方法[12]，同时仅使用它们0.01%的参数量。 我们的研究进一步探讨了曝光校正对下游任务（如边缘检测）更广泛的影响，展示了其有效缓解不良曝光对低级特征不利影响的能力。源代码和数据集可在此链接公开获取：[此链接需您提供或替换为实际提供的URL]。
URL
https://arxiv.org/abs/2507.17252
