关键词: 深度学习, 生成对抗网络, 图像着色

Abstract:

With the development of multimedia technology, the application of various kinds of image coloring, such as black and white photo coloring, medical image rendering and hand drawing coloring, has been increasing gradually. Most of the traditional coloring algorithms have some disadvantages, such as single coloring mode, poor coloring effect in processing part of data, or relying on manual input information, so this paper designs an image coloring method combining conditional generation countermeasure network and color distribution prediction model. The problem that the color of the generated image tends to be simple is improved by generating the shaded image from the generated antagonistic network and correcting the shaded image from the generated antagonistic network through the predicted value of the prediction model. Finally, a color contrast loss is introduced to further improve the quality of the algorithm in some classification images with low contrast. Comparing experiments on the ImageNet data set show that compared with other traditional methods, this method has better coloring effect on more image classification.

Key words: deep learning, generative adversarial network, image coloring