论文标题
图像修饰的级联亮度和色彩：更像艺术家
Cascade Luminance and Chrominance for Image Retouching: More Like Artist
论文作者
论文摘要
照片修饰旨在调整图像的亮度，对比度和饱和度，以使其在美学上更加可取。但是，艺术家在照片修饰中的行为很难定量分析。通过研究其修饰行为，我们提出了一个两阶段的网络，该网络首先使图像变亮，然后在镀铬平面中丰富它们。从图像EXIF中选择了六个有用的信息作为网络的条件输入。此外，添加了色相调色板损失，以使图像更加生动。基于以上三个方面，亮度 - 镀铬级联网（LCCNET）使机器学习的问题是模仿艺术家在照片中更合理的磨练。实验表明，我们的方法对基准MIT-Adobe Five数据集有效，并且可以实现定量和定性评估的最先进的性能。
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.
