论文标题
深度学习图像着色的颜色空间的影响
Influence of Color Spaces for Deep Learning Image Colorization
论文作者
论文摘要
着色是将灰度图像转换为看起来尽可能自然的颜色图像的过程。多年来，这项任务引起了很多关注。现有的着色方法依赖于不同的颜色空间：RGB，YUV，实验室等。在本章中，我们旨在研究他们对通过训练深层神经网络获得的结果的影响，以回答以下问题：“在基于深度学习的色彩中正确选择正确的色彩空间至关重要？”。首先，我们简要总结了文献，尤其是基于深度学习的方法。然后，我们将与RGB，YUV和实验室颜色空间相同的深神经网络结构获得的结果进行比较。定性和定量分析不能在哪个颜色空间更好地结论。然后，我们根据正在处理的图像及其特异性的类型仔细设计体系结构和评估协议的重要性：强/小轮廓，很少/许多对象，近期/存档图像。
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing colorization methods rely on different color spaces: RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question: "Is it crucial to correctly choose the right color space in deep-learning based colorization?". First, we briefly summarize the literature and, in particular, deep learning-based methods. We then compare the results obtained with the same deep neural network architecture with RGB, YUV and Lab color spaces. Qualitative and quantitative analysis do not conclude similarly on which color space is better. We then show the importance of carefully designing the architecture and evaluation protocols depending on the types of images that are being processed and their specificities: strong/small contours, few/many objects, recent/archive images.
