skip to main content
10.1145/3377929.3389959acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

Evolutionary super-resolution

Published:08 July 2020Publication History

ABSTRACT

Super-resolution increases the resolution of an image. Using evolutionary optimization, we optimize the noise injection of a super-resolution method for improving the results. More generally, our approach can be used to optimize any method based on noise injection.

References

  1. M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. 2012.Google ScholarGoogle ScholarCross RefCross Ref
  2. Y. Blau, R. Mechrez, R. Timofte, T. Michaeli, and L. Zelnik-Manor. The 2018 pirm challenge on perceptual image super-resolution. In Proceedings of the European Conference on Computer Vision (ECCV), pages 0--0, 2018.Google ScholarGoogle Scholar
  3. D. Dai, Y. Wang, Y. Chen, and L. Van Gool. Is image super-resolution helpful for other vision tasks? In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1--9. IEEE, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  4. H. Greenspan. Super-resolution in medical imaging. The computer journal, 52(1):43--63, 2009.Google ScholarGoogle Scholar
  5. M. Haris, G. Shakhnarovich, and N. Ukita. Task-driven super resolution: Object detection in low-resolution images. arXiv preprint arXiv:1803.11316, 2018.Google ScholarGoogle Scholar
  6. V. Hosu, H. Lin, T. Sziranyi, and D. Saupe. Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing, pages 1--1, 2020.Google ScholarGoogle Scholar
  7. J.-B. Huang, A. Singh, and N. Ahuja. Single image super-resolution from transformed self-exemplars. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5197--5206, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  8. C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4681--4690, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  9. K. Nguyen, C. Fookes, S. Sridharan, M. Tistarelli, and M. Nixon. Super-resolution for biometrics: A comprehensive survey. Pattern Recognition, 78:23--42, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Noh, W. Bae, W. Lee, J. Seo, and G. Kim. Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection. In Proceedings of the IEEE International Conference on Computer Vision, pages 9725--9734, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  11. O. Oktay, E. Ferrante, K. Kamnitsas, M. Heinrich, W. Bai, J. Caballero, S. A. Cook, A. De Marvao, T. Dawes, D. P. O'Regan, et al. Anatomically constrained neural networks (acnns): application to cardiac image enhancement and segmentation. IEEE transactions on medical imaging, 37(2):384--395, 2017.Google ScholarGoogle Scholar
  12. S.-J. Park, H. Son, S. Cho, K.-S. Hong, and S. Lee. Srfeat: Single image super-resolution with feature discrimination. In Proceedings of the European Conference on Computer Vision (ECCV), pages 439--455, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  13. N. C. Rakotonirina and A. Rasoanaivo. Esrgan+ : Further improving enhanced super-resolution generative adversarial network. accepted ICASSP, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  14. P. Rasti, T. Uiboupin, S. Escalera, and G. Anbarjafari. Convolutional neural network super resolution for face recognition in surveillance monitoring. In International conference on articulated motion and deformable objects, pages 175--184. Springer, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  15. M. S. Sajjadi, B. Scholkopf, and M. Hirsch. Enhancenet: Single image super-resolution through automated texture synthesis. In Proceedings of the IEEE International Conference on Computer Vision, pages 4491--4500, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  16. J. Shermeyer and A. Van Etten. The effects of super-resolution on object detection performance in satellite imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 0--0, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  17. C. K. Sønderby, J. Caballero, L. Theis, W. Shi, and F. Huszár. Amortised map inference for image super-resolution, 2016.Google ScholarGoogle Scholar
  18. X. Wang, K. Yu, C. Dong, and C. Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 606--615, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  19. X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. C. Loy. Esrgan: Enhanced super-resolution generative adversarial networks. In The European Conference on Computer Vision Workshops (ECCVW), September 2018.Google ScholarGoogle Scholar
  20. R. Zeyde, M. Elad, and M. Protter. On single image scale-up using sparse-representations. In International conference on curves and surfaces, pages 711--730. Springer, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Evolutionary super-resolution

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
      July 2020
      1982 pages
      ISBN:9781450371278
      DOI:10.1145/3377929

      Copyright © 2020 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 July 2020

      Check for updates

      Qualifiers

      • poster

      Acceptance Rates

      Overall Acceptance Rate1,669of4,410submissions,38%

      Upcoming Conference

      GECCO '24
      Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader