Shanghai Institute of Technical Physics, CAS
Thirteen Five national defense research Foundation(Jzx2016-0404/Y72-2), Shanghai Key Laboratory of Criminal Scene Evidence funded Foundation(2017xcwzk08)
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.
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):427~432Copy