In this paper, a simple topology of Capsule Network (CapsNet) is investigated for the problem of image colorization. The generative and segmentation capabilities of the original CapsNet topology, which is proposed for image classification problem, is leveraged for the colorization of the images by modifying the network as follows:1) The original CapsNet model is adapted to map the grayscale input to the output in the CIE Lab colorspace, 2) The feature detector part of the model is updated by using deeper feature layers inherited from VGG-19 pre-trained model with weights in order to transfer low-level image representation capability to this model, 3) The margin loss function is modified as Mean Squared Error (MSE) loss to minimize the image-to-imagemapping. The resulting CapsNet model is named as Colorizer Capsule Network (ColorCapsNet).The performance of the ColorCapsNet is evaluated on the DIV2K dataset and promising results are obtained to investigate Capsule Networks further for image colorization problem.

本研究探讨了一种简单的Capsule网络(CapsNet)拓扑结构，用于图像着色问题。通过修改网络结构，将这个原本用于图像分类问题的CapsNet模型适用于CIE Lab颜色空间下灰度图像到彩色图像的生成和分割任务，利用VGG-19预训练模型的深度特征层次和Mean Squared Error loss函数进行改进优化，获得了不错的结果。

胶囊网络图像彩色化