论文标题
灵活的分段曲线估计图片增强
Flexible Piecewise Curves Estimation for Photo Enhancement
论文作者
论文摘要
本文提出了一种称为Flexicurve的新方法，用于增强照片。与执行图像到图像映射的大多数现有方法（需要昂贵的像素重建）不同，Flexicurve采用输入图像并估算全局曲线以调整图像。调整曲线是专门设计用于执行分段映射的，考虑非线性调整和可不同性的。为了应对现实世界图像中具有挑战性和多样化的照明特性，Flexicurve被配制为多任务框架，以产生不同的估计和相关的置信图。这些估计是适应性融合的，以改善不同地区的局部增强。由于图像到曲线的公式，对于大小为512*512*3的图像，Flexicurve只需一个轻量级网络（150K可训练的参数），并且具有快速的推理速度（单个NVIDIA 2080TI GPU上的83fps）。提出的方法提高了效率，而不会损害原始图像中的增强质量和丢失细节。该方法也很吸引人，因为它不限于配对培训数据，因此可以灵活地从未配对的数据中学习丰富的增强风格。广泛的实验表明，我们的方法在定量和质量上实现了图片增强的最新性能。
This paper presents a new method, called FlexiCurve, for photo enhancement. Unlike most existing methods that perform image-to-image mapping, which requires expensive pixel-wise reconstruction, FlexiCurve takes an input image and estimates global curves to adjust the image. The adjustment curves are specially designed for performing piecewise mapping, taking nonlinear adjustment and differentiability into account. To cope with challenging and diverse illumination properties in real-world images, FlexiCurve is formulated as a multi-task framework to produce diverse estimations and the associated confidence maps. These estimations are adaptively fused to improve local enhancements of different regions. Thanks to the image-to-curve formulation, for an image with a size of 512*512*3, FlexiCurve only needs a lightweight network (150K trainable parameters) and it has a fast inference speed (83FPS on a single NVIDIA 2080Ti GPU). The proposed method improves efficiency without compromising the enhancement quality and losing details in the original image. The method is also appealing as it is not limited to paired training data, thus it can flexibly learn rich enhancement styles from unpaired data. Extensive experiments demonstrate that our method achieves state-of-the-art performance on photo enhancement quantitively and qualitatively.
