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Towards Evolutionary Super-Resolution

  • Michal Kawulok
  • Pawel Benecki
  • Daniel Kostrzewa
  • Lukasz Skonieczny
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)

Abstract

Super-resolution reconstruction (SRR) allows for producing a high-resolution (HR) image from a set of low-resolution (LR) observations. The majority of existing methods require tuning a number of hyper-parameters which control the reconstruction process and configure the imaging model that is supposed to reflect the relation between high and low resolution. In this paper, we demonstrate that the reconstruction process is very sensitive to the actual relation between LR and HR images, and we argue that this is a substantial obstacle in deploying SRR in practice. We propose to search the hyper-parameter space using a genetic algorithm (GA), thus adapting to the actual relation between LR and HR, which has not been reported in the literature so far. The results of our extensive experimental study clearly indicate that our GA improves the capacities of SRR. Importantly, the GA converges to different values of the hyper-parameters depending on the applied degradation procedure, which is confirmed using statistical tests.

Keywords

Genetic algorithm Image processing Super-resolution 

Notes

Acknowledgments

The reported work is a part of the SISPARE project run by Future Processing and funded by European Space Agency. The authors were partially supported by Institute of Informatics funds no. BK-230/RAu2/2017 (MK) and BKM-509/RAu2/2017 (DK).

References

  1. 1.
    Zhu, H., Song, W., Tan, H., Wang, J., Jia, D.: Super resolution reconstruction based on adaptive detail enhancement for ZY-3 satellite images. In: Proceedings ISPRS Congress, pp. 213–217 (2016)Google Scholar
  2. 2.
    Yang, F., Chen, Y., Wang, R., Zhang, Q.: Super-resolution microwave imaging: time-domain tomography using highly accurate evolutionary optimization method. In: Proceedings EuCAP, pp. 1–4. IEEE (2015)Google Scholar
  3. 3.
    Jiang, J., Hu, R., Wang, Z., Han, Z.: Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning. IEEE Trans. Image Process. 23(10), 4220–4231 (2014)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Lukinavičius, G., Umezawa, K., Olivier, N., Honigmann, A., Yang, G., Plass, T., Mueller, V., Reymond, L., Corrêa Jr., I.R., Luo, Z.G., et al.: A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins. Nature Chem. 5(2), 132–139 (2013)CrossRefGoogle Scholar
  5. 5.
    Capel, D., Zisserman, A.: Super-resolution enhancement of text image sequences. In: Proceedings IEEE ICPR, vol. 1, pp. 600–605Google Scholar
  6. 6.
    Demirel, H., Anbarjafari, G.: Discrete wavelet transform-based satellite image resolution enhancement. IEEE Trans. Geosci. Remote Sens. 49(6), 1997–2004 (2011)CrossRefMATHGoogle Scholar
  7. 7.
    Qian, S.E., Chen, G.: Enhancing spatial resolution of hyperspectral imagery using sensor’s intrinsic keystone distortion. IEEE Trans. Geosci. Remote Sens. 50(12), 5033–5048 (2012)CrossRefGoogle Scholar
  8. 8.
    Li, L., Zhang, Y., Tian, Q.: Multi-face location on embedded dsp image processing system. In: Proceedings CISP, vol. 4, pp. 124–128 (2008)Google Scholar
  9. 9.
    Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16817-3_8
  10. 10.
    Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings IEEE CVPR, pp. 5197–5206 (2015)Google Scholar
  11. 11.
    Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)CrossRefGoogle Scholar
  12. 12.
    Liebel, L., Körner, M.: Single-image super resolution for multispectral remote sensing data using convolutional neural networks. In: Proceedings ISPRS Congress, pp. 883–890 (2016)Google Scholar
  13. 13.
    Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings IEEE CVPR, pp. 1646–1654 (2016)Google Scholar
  14. 14.
    Efrat, N., Glasner, D., Apartsin, A., Nadler, B., Levin, A.: Accurate blur models vs. image priors in single image super-resolution. In: Proceedings IEEE ICCV, pp. 2832–2839 (2013)Google Scholar
  15. 15.
    Nasrollahi, K., Moeslund, T.B.: Super-resolution: a comprehensive survey. Mach. Vis. Appl. 25(6), 1423–1468 (2014)CrossRefGoogle Scholar
  16. 16.
    Panagiotopoulou, A., Anastassopoulos, V.: Super-resolution image reconstruction techniques: trade-offs between the data-fidelity and regularization terms. Inf. Fusion 13(3), 185–195 (2012)CrossRefGoogle Scholar
  17. 17.
    Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP. Graph. Mod. Image Process. 53(3), 231–239 (1991)CrossRefGoogle Scholar
  18. 18.
    Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process. 5(6), 996–1011 (1996)CrossRefGoogle Scholar
  19. 19.
    Wang, Y., Fevig, R., Schultz, R.R.: Super-resolution mosaicking of UAV surveillance video. In: Proceedings IEEE ICIP, pp. 345–348. IEEE (2008)Google Scholar
  20. 20.
    Akgun, T., Altunbasak, Y., Mersereau, R.M.: Super-resolution reconstruction of hyperspectral images. IEEE Trans. Image Process. 14(11), 1860–1875 (2005)CrossRefGoogle Scholar
  21. 21.
    Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13(10), 1327–1344 (2004)CrossRefGoogle Scholar
  22. 22.
    Hardie, R.: A fast image super-resolution algorithm using an adaptive wiener filter. IEEE Trans. Image Process. 16(12), 2953–2964 (2007)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Li, F., Jia, X., Fraser, D.: Universal HMT based super resolution for remote sensing images. In: Proceedings IEEE ICIP, pp. 333–336 (2008)Google Scholar
  24. 24.
    Ahrens, B.: Genetic algorithm optimization of superresolution parameters. In: Proceedings GECCO, pp. 2083–2088. ACM (2005)Google Scholar
  25. 25.
    Cheng, M.H., Hwang, K.S., Jeng, J.H., Lin, N.W.: PSO-based fusion method for video super-resolution. J. Sign. Proces. Syst. 73(1), 25–42 (2013)CrossRefGoogle Scholar
  26. 26.
    Wu, B., Li, C., Zhan, X.: Integrating spatial structure in super-resolution mapping of hyper-spectral image. Procedia Eng. 29, 1957–1962 (2012)CrossRefGoogle Scholar
  27. 27.
    Zhong, Y., Zhang, L.: Remote sensing image subpixel mapping based on adaptive differential evolution. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 42(5), 1306–1329 (2012)CrossRefGoogle Scholar
  28. 28.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Proces. 13, 600–612 (2004)CrossRefGoogle Scholar
  29. 29.
    Kawulok, M., Smolka, B.: Texture-adaptive image colorization framework. EURASIP J. Adv. Sign. Proces. 2011(99) (2011)Google Scholar
  30. 30.
    Kawulok, M., Benecki, P., Nalepa, J., Kostrzewa, D., Skonieczny, L.: Towards robust evaluation of super-resolution satellite image reconstruction. In: ACIIDS 2018, Part I. LNAI, vol. 10751. Springer, Cham (2018)Google Scholar
  31. 31.
    Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., Zhang, L.: Image super-resolution: the techniques, applications, and future. Sig. Process. 128, 389–408 (2016)CrossRefGoogle Scholar
  32. 32.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1–4), 259–268 (1992)MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proceedings ICSE, pp. 1–10. IEEE (2011)Google Scholar
  34. 34.
    Fister, I., Fister, I.: On the mutation operators in evolution strategies. In: Fister, I., Fister Jr., I. (eds.) Adaptation and Hybridization in Computational Intelligence. ALO, vol. 18, pp. 69–89. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-14400-9_3
  35. 35.
    Lorenzo, P.R., Nalepa, J., Kawulok, M., Ramos, L.S., Pastor, J.R.: Particle swarm optimization for hyper-parameter selection in deep neural networks. In: Proceedings GECCO, pp. 481–488. ACM, New York (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Future ProcessingGliwicePoland
  2. 2.Silesian University of TechnologyGliwicePoland

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