@inproceedings{0a4349cb1725404cb34cc0cbd9181378,
title = "Remote Sensing Image Colorization Based on Multiscale SEnet GAN",
abstract = "Image colorization technique is to colorize the grayscale images or single-channel images. In the research of image colorization, the coloring of remote sensing images is a challenging problem. This paper proposes a new method of remote sensing image colorization method based on Deep Convolution Generative Adversarial Network (DCGAN). We combine multi-scale convolution with Squeeze-and-Excitation Networks (SEnet) to propose a new model that is applied to the generator of DCGAN. Therefore, the generator not only retains the largest image features in the process of the generating images, but also can adjust the channel weights in the training process. We have compared the proposed method with other image colorization methods, and the results show that the proposed method has a good performance on both human vision and image evaluation indicators on the colorization of remote sensing images.",
keywords = "Feature extraction, Generative adversarial network, Image Colorization, Remote sensing, Squeeze-and-excitation networks",
author = "Min Wu and Xin Jin and Qian Jiang and Lee, {Shin Jye} and Lin Guo and Yide Di and Shanshan Huang and Jinfang Huang",
year = "2019",
month = oct,
doi = "10.1109/CISP-BMEI48845.2019.8965902",
language = "English",
series = "Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Qingli Li and Lipo Wang",
booktitle = "Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019",
address = "United States",
note = "null ; Conference date: 19-10-2019 Through 21-10-2019",
}