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Image Super-Resolution

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Computer Vision

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Synonyms

Image upsampling; Image upscaling; Super-resolution

Related Concepts

Definition

Image super-resolution (SR) problem is to reconstruct a high-resolution image from a given one or more low-resolution sample(s). While non-blind super-resolution methods assume that the exact formulation of the low-resolution image is known, blind algorithms are designed to handle arbitrary images from the real-world scenario.

Background

Digital images appear in various resolutions and may require resizing for specific purposes. For example, a thumbnail image in the PC browser is generated from a high-resolution counterpart. On the other hand, a low-resolution frame in a surveillance video should be enlarged to identify the suspect more accurately. Based on the sampling theory, the former can be effectively done by consecutive low-pass filtering and subsampling. However, the latter is an ill-posed problem since we have to reconstruct the missing...

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References

  1. Agustsson E, Timofte R (2017) Ntire 2017 challenge on single image super-resolution: dataset and study. In: The IEEE conference on computer vision and pattern recognition workshops (CVPRW)

    Google Scholar 

  2. Aly HA, Dubois E (2005) Image up-sampling using total-variation regularization with a new observation model. IEEE Trans Image Process (TIP) 14(10):1647–1659

    Article  Google Scholar 

  3. Bell-Kligler S, Shocher A, Irani M (2019) Blind super-resolution kernel estimation using an internal-gan. In: Advances in neural information processing systems (NeurIPS)

    Google Scholar 

  4. Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: British machine vision conference (BMVC)

    Google Scholar 

  5. Blau Y, Michaeli T (2018) The perception-distortion tradeoff. In: The IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  6. Cai J, Zeng H, Yong H, Cao Z, Zhang L (2019) Toward real-world single image super-resolution: a new benchmark and a new model. In: The IEEE international conference on computer vision (ICCV)

    Google Scholar 

  7. Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell (TPAMI) 38(2):295–307

    Article  Google Scholar 

  8. Huang J-B, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: The IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  9. Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP Graph Models Image Process 53(3):231–239

    Article  Google Scholar 

  10. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: The European conference on computer vision (ECCV). Springer

    Google Scholar 

  11. Kim J, Kwon Lee J, Mu Lee K (2016) Accurate image super-resolution using very deep convolutional networks. In: The IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  12. Kim J, Kwon Lee J, Mu Lee K (2016) Deeply-recursive convolutional network for image super-resolution. In: The IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  13. Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: The IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  14. Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: The IEEE conference on computer vision and pattern recognition workshops (CVPRW)

    Google Scholar 

  15. Maeda S (2020) Unpaired image super-resolution using pseudo-supervision. In: IEEE/CVF conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  16. Schulter S, Leistner C, Bischof H (2015) Fast and accurate image upscaling with super-resolution forests. In: The IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  17. Tian Y, Zhang Y, Fu Y, Xu C (2020) Tdan: temporally-deformable alignment network for video super-resolution. In: The IEEE/CVF conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  18. Timofte R, De Smet V, Van Gool L (2014) A+: adjusted anchored neighborhood regression for fast super-resolution. In: Asian conference on computer vision (ACCV)

    Google Scholar 

  19. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process (TIP) 13(4):600–612

    Article  Google Scholar 

  20. Yang F, Yang H, Fu J, Lu H, Guo B (2020) Learning texture transformer network for image super-resolution. In: The IEEE/CVF conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  21. Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process (TIP) 19(11):2861–2873

    Article  MathSciNet  Google Scholar 

  22. Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: International conference on curves and surfaces

    MATH  Google Scholar 

  23. Zhang K, Zhang Z, Cheng C-W, Hsu WH, Qiao Y, Liu W, Zhang T (2018) Super-identity convolutional neural network for face hallucination. In: The European conference on computer vision (ECCV)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea

    Sanghyun Son

Authors
  1. Sanghyun Son
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  2. Kyoung Mu Lee
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Corresponding author

Correspondence to Sanghyun Son .

Section Editor information

  1. Seoul National University, Seoul, Korea

    Kyoung Mu Lee

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Son, S., Lee, K.M. (2021). Image Super-Resolution. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_838-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_838-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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