论文标题
朝着强劲的弱光图像增强
Towards Robust Low Light Image Enhancement
论文作者
论文摘要
在本文中，我们研究了从野外发现的黑暗图像制作明亮图像的问题。图像是黑暗的，因为它们是在昏暗的环境中拍摄的。它们遭受量化和传感器噪声引起的颜色移位。我们不知道此类图像的真正相机响应功能，也不是原始的。我们使用监督的学习方法，依靠对成像管道的直接模拟来生成可用的数据集用于培训和测试。在许多标准数据集上，我们的方法在定量上优于艺术的状态。定性比较表明重建准确性的有力提高。
In this paper, we study the problem of making brighter images from dark images found in the wild. The images are dark because they are taken in dim environments. They suffer from color shifts caused by quantization and from sensor noise. We don't know the true camera reponse function for such images and they are not RAW. We use a supervised learning method, relying on a straightforward simulation of an imaging pipeline to generate usable dataset for training and testing. On a number of standard datasets, our approach outperforms the state of the art quantitatively. Qualitative comparisons suggest strong improvements in reconstruction accuracy.
