Soft Computing

, Volume 22, Issue 5, pp 1385–1398 | Cite as

Multiple dictionary pairs learning and sparse representation-based infrared image super-resolution with improved fuzzy clustering

  • Xiaomin Yang
  • Wei Wu
  • Kai Liu
  • Weilong Chen
  • Zhili Zhou


Exploring sparse representation to enhance the resolution of infrared image has attracted much attention in the last decade. However, conventional sparse representation-based super-resolution aim at learning a universal and efficient dictionary pair for image representation. However, considering that a large number of different structures exist in an image, it is insufficient and unreasonable to present various image structures with only one universal dictionary pair. In this paper, we propose an improved fuzzy clustering and weighted scheme reconstruction framework to solve this problem. Firstly, the training patches are divided into multiple clusters by joint learning multiple dictionary pairs with improved fuzzy clustering method. The goal of joint learning is to learn the multiple dictionary pairs which could collectively represent all the training patches with smallest reconstruction error. So that the learned dictionary pairs are more precise and mutually complementary. Then, high-resolution (HR) patches are estimated according to several most accurate dictionary pairs. Finally, these estimated HR patches are integrated together to generate a final HR patch by a weighted scheme. Numerous experiments demonstrate that this framework outperforms some state-of-art super-resolution methods in both quantitatively and perceptually.


Multi-sensor Super-resolution Sparse representation Infrared image Dictionary learning Multiview representation Fuzzy clustering theory 



The research is sponsored by the National Natural Science Foundation of China (Nos. 61701327, 61711540303 and 61473198), also is supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) Fund, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET) Fund.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Xiaomin Yang
    • 1
  • Wei Wu
    • 1
  • Kai Liu
    • 2
  • Weilong Chen
    • 3
  • Zhili Zhou
    • 4
  1. 1.College of Electronics and Information EngineeringSichuan UniversityChengduPeople’s Republic of China
  2. 2.School of Electrical Engineering and InformationSichuan UniversityChengduPeople’s Republic of China
  3. 3.College of Movie and MediaSichuan Normal UniversityChengduPeople’s Republic of China
  4. 4.Jiangsu Engineering Center of Network Monitoring and School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China

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