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Multi-frame Super Resolution Using Enhanced Papoulis-Gerchberg Method

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Abstract

The research work, discusses the Papoulis-Gerchberg method with re-spect to image restoration procedure called super resolution image reconstruction. This underlined work also demonstrates that Papoulis-Gerchberg performs well only in certain conditions. Modifications are proposed to the Papoulis-Gerchberg method to obtain better super resolution results. The proposed modifications overcome the restrictions of Papoulis-Gerchberg method making it possible on a wide range of images. The suggested modification not only improves the quality of the image but also reduces the computation complexity. The proposed method has greater advantage and high computation speed which is most needed for most of the applications in the real world. The results for the modified Papoulis-Gerchberg are presented to demonstrate its performance.

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References

  1. Komatsu, T., Igarashi, T., Aizawa, K., Saito, T.: Very high resolution imaging scheme with multiple different-aperture cameras. Sig. Process. Image Commun. 5(93), 511–526 (1993). https://doi.org/10.1016/0923-5965(93)90014-K

    Article  Google Scholar 

  2. Hardeep, P., Prashant, B., Joshi, S.M.: A survey on techniques and challenges in image super resolution reconstruction. Int. J. Comput. Sci. Mobile Comput. 2(4), 317–325 (2013)

    Google Scholar 

  3. Babacan, S.D., Molina, R., Katsaggelos, A.K.: Variational Bayesian super resolution. IEEE Trans. Image Process. 20(4), 984–999 (2011). https://doi.org/10.1109/TIP.2010.2080278

    Article  MathSciNet  MATH  Google Scholar 

  4. Tom, B.C., Katsaggelos, A.K.: Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images. In: Proceedings of Interntional Conference on Image Process, pp. 539–542. IEEE, Washington, DC, USA, February 1995

    Google Scholar 

  5. Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process. 5(6), 996–1011 (1996). https://doi.org/10.1109/83.503915

    Article  Google Scholar 

  6. Belekos, S.P., Galatsanos, N.P., Katsaggelos, A.K.: Maximum a posteriori video super-resolution using a new multichannel image prior. IEEE Trans. Image Process. 19(6), 1451–1464 (2010). https://doi.org/10.1109/TIP.2010.2042115

    Article  MathSciNet  MATH  Google Scholar 

  7. Elad, M., Feuer, A.: Restoration of a single superresolution image from several blurred, noisy, and under sampled measured images. IEEE Trans. Image Process. 6(12), 1646–1658 (1997). https://doi.org/10.1109/83.650118

    Article  Google Scholar 

  8. Nguyen, N., Milanfar, P.: A wavelet-based interpolation-restoration method for super resolution (wavelet super resolution). Circuits Syst. Signal Process. 19(4), 321–338 (2000). https://doi.org/10.1007/BF01200891

    Article  MATH  Google Scholar 

  9. Sundar, K.J.A., Divyalakhsmi, K., Ahmed, M.I., Sivagami, R., Sangeetha, V., Vaithiyanathan, V.: Super resolution image reconstruction using frequency spectrum. Indian J. Sci. Technol. 8(35), 1–5 (2015)

    Google Scholar 

  10. Sundar, K.J.A., Vaithiyanathan, V., Manickavasagam, M., Sarkar, A.K.: Enhanced singular value decomposition based fusion for super resolution image reconstruction. Defence Sci. J. 65(6), 459–465 (2015). https://doi.org/10.14429/dsj.65.8336

    Article  Google Scholar 

  11. Sundar, K.J.A., Vaithiyanathan, V.: Multi-frame super-resolution using adaptive normalized convolution. Sig. Image Video Process. 11(2), 357–362 (2017)

    Article  Google Scholar 

  12. Fermüller, C.: Robust wavelet-based super-resolution reconstruction: theory and algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 649–660 (2009). https://doi.org/10.1109/TPAMI.2008.103

    Article  Google Scholar 

  13. Sundar, K.J.A., Vaithiyanathan, V., Thangadurai, G.R.S., Namdeo, N.: Design and analysis of fusion algorithm for multi-frame super-resolution image reconstruction using framelet. Defence Sci. J. 65(4), 292–299 (2015)

    Article  Google Scholar 

  14. Sundar, KJ.A., Jahnavi, M., Lakshmisaritha, K.: Multi-sensor image fusion based on empirical wavelet transform. In: International Conference on Electrical, Electronics, Communication Computer Technologies and Optimization Techniques, ICEECCOT, Mysuru, India, pp. 93–97 (2017)

    Google Scholar 

  15. Danielyan, A., Foi, A., Katkovnik, V., Egiazarian, K.: Image and video super-resolution via spatially adaptive block-matching filtering. In: Proceedings of International Workshop on Local and Non-local Approximation in Image Process, pp. 1–8 (2008)

    Google Scholar 

  16. Takeda, H., Milanfar, P., Protter, M., Elad, M.: Super-resolution without explicit subpixel motion estimation. IEEE Trans. Image Process. 18(9), 1958–1975 (2009). https://doi.org/10.1109/TIP.2009.2023703

    Article  MathSciNet  MATH  Google Scholar 

  17. Santosh, K.C., Wendling, L., Antani, S., Thoma, G.R.: Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell. Syst. 31(3), 66–75 (2016)

    Article  Google Scholar 

  18. Santosh, K.C., Roy, P.P.: Arrow detection in biomedical images using sequential classifier. Int. J. Mach. Learn. Cybern. 9(6), 993–1006 (2018)

    Article  Google Scholar 

  19. Papoulis, A.: A new algorithm in spectral analysis and band-limited extrapolation. IEEE Trans. Circ. Syst. 22(9), 735–742 (1975). https://doi.org/10.1109/TCS.1975.1084118

    Article  MathSciNet  Google Scholar 

  20. Gerchberg, R.W.: Super-resolution through error energy reduction. Opt. Acta Int. J. Opt. 21(9), 709–720 (1974). https://doi.org/10.1080/713818946

    Article  Google Scholar 

  21. Vandewalle, P., Susstrunk, S.: Super resolution images reconstructed from aliased images. In: Proceedings of SPIE/IS&T Visual Communications and Image Processing Conference, Lugano, Switzerland, pp. 1398–1405 (2003). https://doi.org/10.1117/12.506874

  22. Feichtenhofer, C., Fassold, H., Schallauer, P.: A perceptual image sharpness metric based on local edge gradient analysis. IEEE Sig. Process. Lett. 20(12), 379–382 (2013)

    Article  Google Scholar 

  23. Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Sig. Process. Lett. 17(5), 513–516 (2010). https://doi.org/10.1109/LSP.2010.2043888

    Article  Google Scholar 

  24. Milanfar, P: MDSP super-resolution and demosaicing datasets \([\)Online\(]\). http://users.soe.ucsc.edu/~milanfar/software/srdatasets.html. Accessed Sept 2018

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Correspondence to K. Joseph Abraham Sundar .

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Sundar, K.J.A., Sekar, R. (2019). Multi-frame Super Resolution Using Enhanced Papoulis-Gerchberg Method. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_57

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  • DOI: https://doi.org/10.1007/978-981-13-9181-1_57

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