Single frame infrared image super-resolution algorithm based on generative adversarial nets
Received:December 12, 2017  Revised:May 21, 2018  download
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Author NameAffiliationE-mail
SHAO Bao-Tai Shanghai Institute of Technical Physics, Chinese Academy of Sciences shaobaotai@sina.com 
TANG Xin-Yi   
JIN Lu   
LI Zheng   
Abstract:Image processing makes super-resolution infrared image reconstruction effectively improve infrared images resolution, w hich breaks through hardw are performance limits. Based on deep learning, super-resolution method is applied to infrared image, w hich enables the super-resolution reconstruction of single-frame infrared image. Thus, better evaluation results are acquired. Derived from adversarial thoughts, adding a loss function based on discriminant netw ork can improve magnification, w hich can access to better high-frequency details of the restoration and can sharpen image edge and avoid blurred super-resolution infrared images.
keywords:infrared image, super resolution, deep learning, GAN
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Copyright:《Journal of Infrared And Millimeter Waves》