Photo retouching aims to adjust the luminance, contrast, and saturation of the image to make it more human aesthetically desirable. However, artists' actions in photo retouching are difficult to quantitatively analyze. By investigating their retouching behaviors, we propose a two-stage network that brightens images first and then enriches them in the chrominance plane. Six pieces of useful information from image EXIF are picked as the network's condition input. Additionally, hue palette loss is added to make the image more vibrant. Based on the above three aspects, Luminance-Chrominance Cascading Net(LCCNet) makes the machine learning problem of mimicking artists in photo retouching more reasonable. Experiments show that our method is effective on the benchmark MIT-Adobe FiveK dataset, and achieves state-of-the-art performance for both quantitative and qualitative evaluation.
翻译:照片重新触摸的目的是调整图像的亮度、 对比度和饱和度, 以使图像更符合人类的审美性。 然而, 艺术家在照片重触中的行为很难进行定量分析 。 通过调查他们的重触行为, 我们提议了一个两阶段网络, 先亮亮图像, 然后在色度平面上丰富图像。 从图像 EXIF 中提取了六块有用的信息作为网络的条件输入 。 此外, 添加了色调损失, 以使图像更具活力 。 根据上述三个方面, Luminance- Chroncance Cascading Net (LCCNet) 使得在照片重触摸中模仿艺术家的机器学习问题更加合理。 实验显示, 我们的方法在MIT- Adobe FiveK 数据集的基准上是有效的, 并实现了定量和定性评估的最新表现。