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Blind single image super resolution with low computational complexity

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Abstract

This paper proposes a single image super resolution algorithm with the aim of satisfying three desirable characteristics, namely, high quality of the produced images, adaptability to image contents and unknown blurring conditions used to generate given input images, and low computational complexity. After the given input image is up-scaled using a conventional reconstruction operator, the missing high frequency components estimated from lower resolution versions of the input image are added for improved quality and, moreover, the amount of the high frequency components to be added is adaptively determined. No computationally intensive operation is involved in the whole process, which makes the method computationally cheap. Experimental results show that the proposed method yields good subjective and objective image quality consistently across different blurring conditions and contents, and operates fast in comparison to existing state-of-the-art algorithms. In addition, it is also demonstrated that the proposed method can be used in combination with the existing algorithms in order to improve further their performance in terms of image quality.

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Acknowledgments

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “IT Consilience Creative Program” (IITP-2015-R0346-15-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion).

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Authors and Affiliations

  1. Yonsei Institute of Convergence Technology, Yonsei University, 85 Songdogwahak-ro, Yeonsu-gu, Incheon, South Korea

    Won-Hee Kim

  2. School of Integrated Technology & Yonsei Institute of Convergence Technology, Yonsei University, 85 Songdogwahak-ro, Yeonsu-gu, Incheon, South Korea

    Jong-Seok Lee

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  1. Won-Hee Kim
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  2. Jong-Seok Lee
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Correspondence to Jong-Seok Lee.

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Kim, WH., Lee, JS. Blind single image super resolution with low computational complexity. Multimed Tools Appl 76, 7235–7249 (2017). https://doi.org/10.1007/s11042-016-3396-0

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  • DOI: https://doi.org/10.1007/s11042-016-3396-0

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