• Journal of Infrared and Millimeter Waves
  • Vol. 39, Issue 3, 388 (2020)
Shuo HUANG1、2、3, Yong HU1、3、*, Cai-Lan GONG1、3, and Fu-Qiang ZHENG1、2、3
Author Affiliations
  • 1Shanghai Institute of Technical Physics, Chinese Academy of Science , Shanghai200083, China
  • 2University of Chinese Academy of Sciences, Beijing100049, China
  • 3CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai20008, China
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    DOI: 10.11972/j.issn.1001-9014.2020.03.018 Cite this Article
    Shuo HUANG, Yong HU, Cai-Lan GONG, Fu-Qiang ZHENG. Salience region super-resolution reconstruction algorithm for infrared images based on sparse coding[J]. Journal of Infrared and Millimeter Waves, 2020, 39(3): 388 Copy Citation Text show less

    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. By combining the saliency detection and the super-segment reconstruction, it 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 while the background region adopts Gaussian filtering. Experimental results show that the improved reconstruction algorithm is better than ScSR and SRCNN under the same conditions. The image signal-to-noise ratio is increased by 3-4 times.
    Shuo HUANG, Yong HU, Cai-Lan GONG, Fu-Qiang ZHENG. Salience region super-resolution reconstruction algorithm for infrared images based on sparse coding[J]. Journal of Infrared and Millimeter Waves, 2020, 39(3): 388
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