Single frame infrared image super-resolution algorithm based on generative adversarial nets
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Shanghai Institute of Technical Physics, CAS

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Thirteen Five national defense research Foundation(Jzx2016-0404/Y72-2), Shanghai Key Laboratory of Criminal Scene Evidence funded Foundation(2017xcwzk08)

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    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.

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SHAO Bao-Tai, TANG Xin-Yi, JIN Lu, LI Zheng. Single frame infrared image super-resolution algorithm based on generative adversarial nets[J]. Journal of Infrared and Millimeter Waves,2018,37(4):427~432

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History
  • Received:December 12,2017
  • Revised:May 21,2018
  • Adopted:February 08,2018
  • Online: August 30,2018
  • Published: