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Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image

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Abstract

Super-resolution (SR) methods are effective for generating a high-resolution image from a single low-resolution image. However, four problems are observed in existing SR methods. (1) They cannot reconstruct many details from a low-resolution infrared image because infrared images always lack detailed information. (2) They cannot extract the desired information from images because they do not consider that images naturally come at different scales in many cases. (3) They fail to reveal different physical structures of low-resolution patch because they extract features from a single view. (4) They fail to extract all the different patterns because they use only one dictionary to represent all patterns. To overcome these problems, we propose a novel SR method for infrared images. First, we combine the information of high-resolution visible light images and low-resolution infrared images to improve the resolution of infrared images. Second, we use multiscale patches instead of fixed-size patches to represent infrared images more accurately. Third, we use different feature vectors rather than a single feature to represent infrared images. Finally, we divide training patches into several clusters, and multiple dictionaries are learned for each cluster to provide each patch with a more accurate dictionary. In the proposed method, clustering information for low-resolution patches is learnt by using fuzzy clustering theory. Experiments validate that the proposed method yields better results in terms of quantization and visual perception than the state-of-the-art algorithms.

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Acknowledgments

The research is sponsored by the National Natural Science Foundation of China(No. 61271330, No. 61473198), also is supported by the Priority Academic Program Development of Jiangsu Higer Education Institutions(PAPD) Fund, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET) Fund.

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610064, People’s Republic of China

    Xiaomin Yang & Wei Wu

  2. School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, 610064, People’s Republic of China

    Kai Liu

  3. College of Movie and Media, Sichuan Normal University, Chengdu, Sichuan, 610018, People’s Republic of China

    Weilong Chen

  4. Graphic Image and Signal Processing Applications Laboratory, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, People’s Republic of China

    Ping Zhang

  5. Jiangsu Engineering Center of Network Monitoring and School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, People’s Republic of China

    Zhili Zhou

Authors
  1. Xiaomin Yang
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  2. Wei Wu
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  3. Kai Liu
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  4. Weilong Chen
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Correspondence to Wei Wu.

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Yang, X., Wu, W., Liu, K. et al. Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image. Multimed Tools Appl 76, 24871–24902 (2017). https://doi.org/10.1007/s11042-017-4639-4

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  • DOI: https://doi.org/10.1007/s11042-017-4639-4

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