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http://dx.doi.org/10.9728/dcs.2018.19.6.1197

A Study on Super Resolution Image Reconstruction for Acquired Images from Naval Combat System using Generative Adversarial Networks  

Kim, Dongyoung (Agency for Defense Development)
Publication Information
Journal of Digital Contents Society / v.19, no.6, 2018 , pp. 1197-1205 More about this Journal
Abstract
In this paper, we perform Single Image Super Resolution(SISR) for acquired images of EOTS or IRST from naval combat system. In order to conduct super resolution, we use Generative Adversarial Networks(GANs), which consists of a generative model to create a super-resolution image from the given low-resolution image and a discriminative model to determine whether the generated super-resolution image is qualified as a high-resolution image by adjusting various learning parameters. The learning parameters consist of a crop size of input image, the depth of sub-pixel layer, and the types of training images. Regarding evaluation method, we apply not only general image quality metrics, but feature descriptor methods. As a result, a larger crop size, a deeper sub-pixel layer, and high-resolution training images yield good performance.
Keywords
Deep learning; Generative Adversarial Networks; Naval combat system; SISR; SRGAN;
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Times Cited By KSCI : 1  (Citation Analysis)
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