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

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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.

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Authors and Affiliations

  1. School of Information Science and Technology of Northwest University, Xi’an, People’s Republic of China

    Xuan Zhu, Peng Jin, XianXian Wang & Na Ai

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  1. Xuan Zhu
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  2. Peng Jin
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  3. XianXian Wang
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  4. Na Ai
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Correspondence to Xuan Zhu.

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Zhu, X., Jin, P., Wang, X. et al. Multi-frame image super-resolution reconstruction via low-rank fusion combined with sparse coding. Multimed Tools Appl 78, 7143–7154 (2019). https://doi.org/10.1007/s11042-018-6495-2

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  • DOI: https://doi.org/10.1007/s11042-018-6495-2

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