Remote Sensing Image Colorization Based on Multiscale SEnet GAN

Min Wu, Xin Jin, Qian Jiang, Shin Jye Lee, Lin Guo, Yide Di, Shanshan Huang, Jinfang Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
EditorsQingli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728148526
DOIs
StatePublished - Oct 2019
Event12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 - Huaqiao, China
Duration: 19 Oct 201921 Oct 2019

Publication series

NameProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

Conference

Conference12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
CountryChina
CityHuaqiao
Period19/10/1921/10/19

Keywords

  • Feature extraction
  • Generative adversarial network
  • Image Colorization
  • Remote sensing
  • Squeeze-and-excitation networks

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