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Multiframe image superresolution based on cepstral analysis

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Abstract

In this paper, multiframe image superresolution algorithm based on cepstral analysis has been proposed. The multiple low-resolution images are registered by calculating the translational, rotational and scaling shifts in the cepstral domain. The registered multiple low-resolution images are projected onto a high-resolution grid to generate a new high-resolution image. The projection of low-resolution image causes blurred and jaggy edges in the high-resolution image. To de-blur and regularize high-resolution image, Wiener filter is designed using point spread function computed with the help of the shaken length N and shaken angle θ that are obtained from cepstral analysis. Performance of the proposed algorithm is evaluated using different benchmark images in terms of subjective results and objective parameters such as PSNR and computation time. The subjective and objective results show the superiority of the proposed algorithm over the conventional and state-of-the-art superresolution techniques.

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References

  1. Park, S.C., Park, M.K., Kang, M.G.: Superresolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 3, 21–36 (2003)

    Article  Google Scholar 

  2. Chan, J.C.W., Ma, J., Canters, F.: A comparison of superresolution reconstruction methods for multi-angle CHRIS/PROBA images. Proc. SPIE Remote Sens. Int. Soc. Opt. Photon. 7109, 710904-1–710904-11 (2008)

    Google Scholar 

  3. Ma, J., Chan, J.C.W.: Superresolution reconstruction of hyperspectral remote sensing imagery using constrained optimization of POCS. In: Proceeding of IEEE Geoscience Remote Sensing Symposium (IGARSS), pp. 7271–7274 (2012)

  4. Li, H., Lam, K.-M.: Guided iterative back-projection scheme for single-image super-resolution. In: IEEE Global High Tech Congress on Electronics (GHTCE), pp. 175–180 (2013)

  5. Zomet, A., Rav-Acha, A., Peleg, S.: Robust super-resolution. In: Proceedings International Conference on Computer Vision and Pattern Recognition (CVPR) (2001)

  6. Pham, T.Q., van Vliet, L.J., Schutte, K.: Robust fusion of irregularly sampled data using adaptive normalized convolution. EURASIP J. Appl. Signal Process. 2006, Article ID 83268 (2006)

  7. Cheng, M.-H., Lin, N.-W., Hwang, K.-S., Jeng, J.-H.: Fast Video Super-Resolution Using Artificial Neural Networks. In: IEEE, IET International Symposium on Communication Systems, Networks and Digital Signal Processing (2012)

  8. Lu, X., Yuan, Y., Yan, P.: Alternatively constrained dictionary learning for image superresolution. IEEE Trans. Cybern. 44(3), 366–377 (2014)

    Article  Google Scholar 

  9. Bayir, O., Eksioglu, E.M.: Analysis Sparsity Based Single Image Superresolution. In: IEEE Conference on Signal Processing and Communication Application, pp. 977–980 (2016)

  10. Haris, M., Watanabe, T., Fan, L., Widyanto, M.R., Nobuhara, H.: Superresolution for UAV images via adaptive multiple sparse representation and its application to 3-D reconstruction. IEEE Trans. Geosci. Remote Sens. 55, 4047–4058 (2017)

    Article  Google Scholar 

  11. Patel, R.C., Joshi, M.V.: Super-resolution of hyperspectral images: use of optimum wavelet filter coefficients and sparsity regularization. IEEE Trans. Geosci. Remote Sens. 53(4), 1728–1736 (2015)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Kappeler, A., Yoo, S., Dai, Q, Katsaggelos, A.K.: Super-resolution of compressed videos using convolutional neural networks. IEEE Int. Conf. Image Process., pp. 1150–1154 (2016)

  14. Cheong, J.Y., Park, I.K.: Deep CNN-based super-resolution using external and internal examples. IEEE Signal Process. Lett. 24, 1070–9908 (2017)

    Article  Google Scholar 

  15. Nazzal, M., Ozkaramanli, H.: Wavelet domain dictionary learning-based single image superresolution. SIViP 9(7), 1491–1501 (2015)

    Article  Google Scholar 

  16. Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., Zhang, L.: Image super-resolution: the techniques, applications, and future. Signal Process. 128, 389–408 (2016)

    Article  Google Scholar 

  17. Rom, R.: On the cepstrum of two-dimensional functions. IEEE Trans. Inf. Theory 21, 214–217 (1975)

    Article  MathSciNet  Google Scholar 

  18. Kedo, K.: Digital Fourier Analysis: Advanced Techniques. Springer, Berlin (2015)

    Google Scholar 

  19. Shi, M.: Image Restoration Using the Alternation Direction Method Based on the Gradient Cepstrum Analysis PSF Estimation Strategy. In: Proceeding of the International Conference on Communications, Signal Processing and Systems, pp. 725–732 (2016)

  20. Cakir, S., Cetin, A.E.: Mel-cepstral feature extraction methods for image representation. Opt. Eng. 49(9), 097004 (2010)

    Article  Google Scholar 

  21. Kim, C.W., Ansari, R., Cetin, A.E.: A Class of Linear-Phase Regular Biorthogonal Wavelets. In: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992. ICASSP-92, vol. 4, pp. 673–676 (1992)

  22. Narwaria, M., Lin, W., Enis Cetin, A.: Scalable image quality assessment with 2D mel-cepstrum and machine learning approach. Pattern Recogn. 45, 299–313 (2012)

    Article  Google Scholar 

  23. Li, Y.-J., Di, X.-G.: Image Mixed Blur Classification and Parameter Identification Based On Cepstrum Peak Detection. In: 35th Chinese Control Conference (CCC) (2016)

  24. Rabiner, L., Juang, B.: Fundamentals of Speech Recognition. Prentice-Hall Inc, Upper Saddle River (1993)

    Google Scholar 

  25. Chu, C.-H.: Superresolution Image Reconstruction for Mobile Devices. Springer, Berlin (2012)

    Google Scholar 

  26. Rahulkar, A.D., Holambe, R.S.: Half-iris feature extraction and recognition using a new class of biorthogonal triplet half band filter bank and flexible k-out-of-n: a post-classifier. IEEE Trans. Inf. Forensic Secur. 7, 230–240 (2012)

    Article  Google Scholar 

  27. Anbarjafari, G., Demirel, H.: Image superresolution based on interpolation of wavelet domain high frequency subbands and spatial domain input image. IEEE Trans. Image Process. 20(5), 1458–1460 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  28. Patil, B.D., Patwardhan, P.G., Gadre, V.M.: On the design of FIR wavelet filter banks using factorization of a half band polynomial. IEEE Signal Process. Lett. 15, 485–488 (2008)

    Article  Google Scholar 

  29. Ansari, R., Kim, C.W., Dedovic, M.: Structure and design of two-channel filter banks derived from a triplet of halfband filters. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 46(12), 1487–1496 (1999)

    Article  Google Scholar 

  30. Chopade, P.B., Patil, P.M.: Hybrid-thresholding Based Image superresolution technique by the use of triplet half band wavelets. J. Inst. Eng. Ser. B 97(4), 517–523 (2016)

    Article  Google Scholar 

  31. Chopade, P.B., Patil, P.M.: Design of dyadic integer coefficient based biorthogonal wavelet filters for image superresolution using image sub-pixel image. ICTACT J. 4(4), 817–823 (2014)

    Google Scholar 

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Authors and Affiliations

  1. MES’s CoE, Pune, 411001, India

    Pravin B. Chopade

  2. JSPM’s JSCOE, Pune, 411058, India

    Pradeep M. Patil

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  1. Pravin B. Chopade
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  2. Pradeep M. Patil
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Correspondence to Pravin B. Chopade.

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Chopade, P.B., Patil, P.M. Multiframe image superresolution based on cepstral analysis. SIViP 13, 199–207 (2019). https://doi.org/10.1007/s11760-018-1346-1

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  • DOI: https://doi.org/10.1007/s11760-018-1346-1

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