液晶与显示, 2020, 35 (3): 272, 网络出版: 2020-05-12  

基于密集连接生成对抗网络的图像颜色迁移

Image color transfer with dense connections generative adversarial networks
作者单位
长春理工大学 电子信息工程学院, 吉林 长春 130022
摘要
针对传统颜色迁移算法在处理图像时存在颜色误传递, 色彩不够自然等问题, 提出一种基于密集连接生成对抗网络的图像颜色迁移方法。在训练过程中, 训练生成网络生成颜色迁移图像。生成网络中的编码层利用密集连接网络跨层连接的优点促进颜色特征重用, 加快网络的收敛速度, 同时在转换层采用3层残差模块代替原始的两层残差模块更好地组合图像的不同特征。训练判别网络使其辨别原图像与生成的迁移图像间的差别。本文判别网络中用-log函数计算模型损失, 加快训练初期更新速度。实验结果表明, 与同类模型相比, 本文方法结果图像保留更多细节, 且能够抑制部分噪声, 整体更接近自然图像。
Abstract
Aiming at the problem that the traditional color transfer algorithm has color mis-transmission and unnaturalness as processing images, an image color transfer method based on dense connection generative adversarial network is proposed. During the training process, the training generation network generates the color transfer image. The coding layer in the generated network promotes the reuse of color features and speeds up the convergence rate of the network by using the cross-layer connection of the dense connection network. The conversion layer uses a three-layer residual module instead of the original two-layer residual module to combine different features of the image. The discriminating network is trained to distinguish the difference between the original image and the generated transfer image. The -log function is used to calculate the model loss in the network and speed up the initial update of the training. The experimental results show that the result image of this method retains more details compared with the similar model, can suppress some noise and the whole image is closer to the natural image.

王晓宇, 朱一峰, 郗金洋, 王尧, 段锦. 基于密集连接生成对抗网络的图像颜色迁移[J]. 液晶与显示, 2020, 35(3): 272. WANG Xiao-yu, ZHU Yi-feng, XI Jin-yang, WANG Yao, DUAN Jin. Image color transfer with dense connections generative adversarial networks[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(3): 272.

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