标题： UNICE：训练一个通用的图像对比度增强器 显示英文标题

标题： UNICE: Training A Universal Image Contrast Enhancer

摘要： 现有的图像对比度增强方法通常针对特定任务进行设计，例如欠/过曝校正、低光和逆光图像增强等。 然而，这些学习到的模型在不同任务之间，甚至在同一任务的不同数据集之间，都表现出较差的泛化性能。 探索是否可以为各种对比度增强任务学习一个通用且泛化的模型是重要的。 在本工作中，我们观察到这些任务的共同关键因素在于需要进行曝光和对比度调整，如果能够获得高动态范围（HDR）输入，这一问题可以得到很好的解决。 因此，我们从公开来源收集了46,928张HDR原始图像，并通过多曝光融合生成328,496张sRGB图像，以构建多曝光序列（MES）和相应的伪sRGB真实标签。 结果，我们训练了一个网络，从单张sRGB图像生成MES，然后训练另一个网络将生成的MES融合为增强图像。 我们提出的方法，即UNiversal Image Contrast Enhancer（UNICE），无需昂贵的人工标注。 然而，它在不同任务之间以及同一任务内部的泛化性能明显强于现有的图像对比度增强方法，甚至在多个无参考图像质量指标中超过了人工创建的真实标签。 数据集、代码和模型可在https://github.com/BeyondHeaven/UNICE获取。 显示英文摘要

摘要： Existing image contrast enhancement methods are typically designed for specific tasks such as under-/over-exposure correction, low-light and backlit image enhancement, etc. The learned models, however, exhibit poor generalization performance across different tasks, even across different datasets of a specific task. It is important to explore whether we can learn a universal and generalized model for various contrast enhancement tasks. In this work, we observe that the common key factor of these tasks lies in the need of exposure and contrast adjustment, which can be well-addressed if high-dynamic range (HDR) inputs are available. We hence collect 46,928 HDR raw images from public sources, and render 328,496 sRGB images to build multi-exposure sequences (MES) and the corresponding pseudo sRGB ground-truths via multi-exposure fusion. Consequently, we train a network to generate an MES from a single sRGB image, followed by training another network to fuse the generated MES into an enhanced image. Our proposed method, namely UNiversal Image Contrast Enhancer (UNICE), is free of costly human labeling. However, it demonstrates significantly stronger generalization performance than existing image contrast enhancement methods across and within different tasks, even outperforming manually created ground-truths in multiple no-reference image quality metrics. The dataset, code and model are available at https://github.com/BeyondHeaven/UNICE.