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Volume: 32 | Article ID: art00004
High-quality multispectral image generation using Conditional GANs
  DOI :  10.2352/ISSN.2470-1173.2020.8.IMAWM-086  Published OnlineJanuary 2020

In this paper, we demonstrate the use of a Conditional Generative Adversarial Networks (cGAN) framework for producing high-fidelity, multispectral aerial imagery using low-fidelity imagery of the same kind as input. The motivation behind is that it is easier, faster, and often less costly to produce low-fidelity images than high-fidelity images using the various available techniques, such as physics-driven synthetic image generation models. Once the cGAN network is trained and tuned in a supervised manner on a data set of paired low- and high-quality aerial images, it can then be used to enhance new, lower-quality baseline images of similar type to produce more realistic, high-fidelity multispectral image data. This approach can potentially save significant time and effort compared to traditional approaches of producing multispectral images.

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Ayush Soni, Alexander Loui, Scott Brown, Carl Salvaggio, "High-quality multispectral image generation using Conditional GANsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2020,  pp 86-1 - 86-7,

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