Image colorization technique is used to colorize the gray-level image or single-channel image, which is a very significant and challenging task in image processing, especially the colorization of remote sensing images. This paper proposes a new method for coloring remote sensing images based on deep convolution generation adversarial network. The adopted generator model is a symmetrical structure using the principle of auto-encoder, and a multi-scale convolutional module is specially designed to introduce into the generator model. Thus, the proposed generator can enable the whole model to retain more image features in the process of up-sampling and down-sampling. Meanwhile, the discriminator uses residual neural network 18 that can compete with the generator, so that the generator and discriminator can effectively optimize each other. In the proposed method, the color space transformation technique is first utilized to convert remote sensing images from RGB to YUV. Then, the Y channel (a gray-level image) is used as the input of the neural network model to predict UV channels. Finally, the predicted UV channels are concatenated with the original Y channel as a whole YUV that is then transformed into RGB space to get the final color image. Experiments are conducted to test the performance of different image colorization methods, and the results show that the proposed method has good performance in both visual quality and objective indexes on the colorization of remote sensing image.

中文翻译：

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YUV颜色空间中使用对称多尺度DCGAN的遥感图像着色

图像着色技术用于对灰度图像或单通道图像进行着色，这是图像处理中一项非常重要且具有挑战性的任务，尤其是遥感图像的着色。本文提出了一种基于深度卷积生成对抗网络的遥感图像着色新方法。采用的生成器模型是利用自编码器原理的对称结构，专门设计了多尺度卷积模块引入生成器模型。因此，所提出的生成器可以使整个模型在上采样和下采样过程中保留更多的图像特征。同时，判别器使用可以与生成器竞争的残差神经网络18，使得生成器和判别器可以有效地相互优化。在所提出的方法中，首先利用色彩空间变换技术将遥感图像从RGB 转换为YUV。然后，将 Y 通道（灰度图像）作为神经网络模型的输入来预测 UV 通道。最后，将预测的 UV 通道与原始 Y 通道连接为一个完整的 YUV，然后将其转换为 RGB 空间以获得最终的彩色图像。通过实验测试了不同图像着色方法的性能，结果表明，该方法在视觉质量和遥感图像着色的客观指标上均具有良好的性能。Y通道（灰度图像）作为神经网络模型的输入来预测UV通道。最后，将预测的 UV 通道与原始 Y 通道连接为一个完整的 YUV，然后将其转换为 RGB 空间以获得最终的彩色图像。通过实验测试了不同图像着色方法的性能，结果表明，该方法在视觉质量和遥感图像着色的客观指标上均具有良好的性能。Y通道（灰度图像）作为神经网络模型的输入来预测UV通道。最后，将预测的 UV 通道与原始 Y 通道连接为一个完整的 YUV，然后将其转换为 RGB 空间以获得最终的彩色图像。通过实验测试了不同图像着色方法的性能，结果表明，该方法在视觉质量和遥感图像着色的客观指标上均具有良好的性能。