Salience region super-resolution reconstruction algorithm for infrared images based on sparse coding
Received:May 20, 2019  Revised:April 15, 2020  download
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Author NameAffiliationE-mail
HUANG Shuo Shanghai Institute of Technical Physics, Chinese Academy of Science , Shanghai 200083, China
University of Chinese Academy of Sciences, Beijing 100049, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai 200083, China 
shuo_huang_sitp@sina.com 
HU Yong Shanghai Institute of Technical Physics, Chinese Academy of Science , Shanghai 200083, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai 200083, China 
huyong@mail.sitp.ac.cn 
GONG Cai-Lan Shanghai Institute of Technical Physics, Chinese Academy of Science , Shanghai 200083, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai 200083, China 
 
ZHENG Fu-Qiang Shanghai Institute of Technical Physics, Chinese Academy of Science , Shanghai 200083, China
University of Chinese Academy of Sciences, Beijing 100049, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai 200083, China 
 
Abstract:Due to the limitations of infrared optical diffraction and infrared detectors, the noise of infrared images is relatively large and the resolution is low. Super-resolution reconstruction of infrared images improves image resolution, but at the same time enhances the noise of background. Aiming at this problem, a salience region super-resolution reconstruction algorithm for infrared images based on sparse coding is proposed. Combining the saliency detection and the super-segment reconstruction improves the target definition and reduces the background noise. Firstly, image feature is extracted by double-layer convolution, and image patches with large entropy are adaptively selected for training the joint dictionary. Sparse features are used to calculate the saliency to obtain salient regions, which reconstructs image patches in saliency region by the trained dictionary, and the background region adopts Gaussian filtering. Experimental results show that the improved reconstruction algorithm is better than ScSR and SRCNN under the same conditions, and the image signal-to-noise ratio is increased by 3-4 times.
keywords:infrared image  saliency detection  sparse coding  sparse features
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Copyright:《Journal of Infrared And Millimeter Waves》