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A Universal Fusion Strategy for Image Super-Resolution Jointly from External and Internal Examples

  • Wei Wang
  • Xuesen Shang
  • Wenming YangEmail author
  • Canrong Zhang
  • Qingmin Liao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)

Abstract

The validity of learning-based image super-resolution is largely limited by supporting dataset. Neither external-based nor internal-based super-resolution methods can perform well in real applications such as medical endoscopic images. This paper studies the strategy of joint learning of two kinds of methods. We first build sub-dictionaries and study the corresponding mapping matrices on the respective samples. Due to the consistency of learning strategies, we establish joint mapping matrices based on the distance between the input low-resolution image patches and the dictionary atoms in the reconstruction phase. We adopt the nearest neighbor strategy and the weighted joint strategy to obtain the new mapping matrix. The high-resolution image is reconstructed by the new mapping model. The experiments prove the effectiveness of our strategy.

Keywords

Super-resolution External examples Internal examples Medical endoscopic images Joint learning 

Notes

Acknowledgment

This work was supported by the Natural Science Foundation of China (Nos. 61471216 and 61771276), the National Key Research and Development Program of China (No. 2016YFB0101001) and the Special Foundation for the Development of Strategic Emerging Industries of Shenzhen (Nos. JCYJ20170307153940960 and JCYJ20170817161845824).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wei Wang
    • 1
  • Xuesen Shang
    • 1
  • Wenming Yang
    • 1
    Email author
  • Canrong Zhang
    • 2
  • Qingmin Liao
    • 1
  1. 1.Department of Electronic Engineering, Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  2. 2.Research Center for Modern Logistics, Graduate School at ShenzhenTsinghua UniversityShenzhenChina

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