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Multi-frame image super-resolution reconstruction via low-rank fusion combined with sparse coding

  • Xuan Zhu
  • Peng Jin
  • XianXian Wang
  • Na Ai
Article
  • 51 Downloads

Abstract

The sparse coding method has been successfully applied to multi-frame super-resolution in recent years. In this paper, we propose a new multi-frame super-resolution framework which combines low-rank fusion with sparse coding to improve the performance of multi-frame super-resolution. The proposed method gets the high-resolution image by a three-stage process. First, a fused low-resolution image is obtained from multi-frame image by the method of registration and low-rank fusion. Then, we use the jointly training method to train a pair of learning dictionaries which have good adaptive ability. Finally, we use the learning dictionaries combined with sparse coding theory to realize super-resolution reconstruction of the fused low-resolution image. As the experiment results show, this method can recover the lost high frequency information, and has good robustness.

Keywords

Super-resolution reconstruction Multi-frame image Low-rank fusion Sparse coding 

Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and Technology of Northwest UniversityXi’anPeople’s Republic of China

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