Saliency region super-resolution reconstruction algorithm for infrared images based on sparse coding
投稿时间:2019-05-20  修订日期:2019-10-17  download
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作者单位E-mail
黄硕 中国科学院上海技术物理研究所 shuo_huang_sitp@sina.com 
胡勇 中国科学院上海技术物理研究所 huyong@mail.sitp.ac.cn 
巩彩兰 中国科学院上海技术物理研究所  
郑付强 中国科学院上海技术物理研究所  
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 saliency 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 saliency to obtain salient region, 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 the reconstruction models 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》