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Spatially High Resolution Visible and Near-Infrared Separation using Conditional Generative Adversarial Network and Color Brightness Transfer Method
- Authors
- Park, Y.[Park, Y.]; Jeon, B.[Jeon, B.]
- Issue Date
- 2018
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- deep learning; GAN; near-infrared; separation
- Citation
- 10th International Conference on Communications, Circuits and Systems, ICCCAS 2018, pp.426 - 430
- Journal Title
- 10th International Conference on Communications, Circuits and Systems, ICCCAS 2018
- Start Page
- 426
- End Page
- 430
- ISSN
- 0000-0000
- Abstract
- Since near-infrared (NIR) image information is useful in improving visible range (VIS) image, acquisition of both images in a more simple and economic way has drawn much research interest. Deep-learning based approach is found to be effective in the separation from a mixed NIR and VIS image captured by a conventional camera, however, it has a problem of high computational complexity, especially for an image of high spatial resolution. In this paper, we propose a method for separating high-resolution VIS and NIR images using a deep-learning based on a conditional generative adversarial network. Experimental results show that the proposed method can reduce the computational complexity by 97 times as compared with the previous work without loss in image quality. © 2018 IEEE.
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Related Researcher
- JEON, BYEUNG WOO
- Information and Communication Engineering (Electronic and Electrical Engineering)