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Single image super-resolution under multi-frame method

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Abstract

The multi-frame image super-resolution method utilizes a series of low-resolution images from the same scene to reconstruct the corresponding high-quality super-resolution image. However, the insufficient input low-resolution images make it incapable of reconstructing the high-resolution image from a single low-resolution image. In this work, we extend the framework of multi-frame image super-resolution to handle the single low-resolution image via rolling guidance filtering. And an improved version of diffusion-driven regularizer-based multi-frame image super-resolution algorithm is proposed and applied on passive millimeter-wave (PMMW) image super-resolution. Specifically, the joint filtering is first exploited to suppress the noise of single low-resolution noisy image. The rolling guidance method is exploited to generate the structurally multi-scale low-resolution images forming the basis of multi-frame image super-resolution. The generated image sequences are then fed to the nonlinear diffusion regularizer-based super-resolution algorithm. The two-directional total variation de-convolution is finally employed to remove the blur, producing a sharp and clear high-resolution image. Experiments demonstrate the effectiveness of the proposed method and show its superiority for the natural and PMMW images.

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Notes

  1. Some codes are available at http://lcav.epfl.ch/software/superresolution.

References

  1. Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 20(3), 21–36 (2003)

    Article  Google Scholar 

  2. Tschumperle, D., Deriche, R.: Vector-valued image regularization with pdes: a common framework for different applications. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 506–517 (2005)

    Article  Google Scholar 

  3. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167–1183 (2002)

    Article  Google Scholar 

  4. Tsai, R., Huang, T.S.: Multiframe image restoration and registration. Adv. Comput. Vis. Image Process. 1(2), 317–339 (1984)

    Google Scholar 

  5. Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: 2009 IEEE 12th International Conference on Computer Vision, 2009, pp. 349–356

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

    Article  Google Scholar 

  7. Tom, B.C., Katsaggelos, A.K.: Reconstruction of a high-resolution image from multiple-degraded misregistered low-resolution images. In: Visual Communications and Image Processing’94, 1994, pp. 971–981

  8. Hardie, R.C., Barnard, K.J., Armstrong, E.E.: Joint map registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Process. 6(12), 1621–1633 (1997)

    Article  Google Scholar 

  9. Liu, X., Chen, L., Wang, W., Zhao, J.: Robust multi-frame super-resolution based on spatially weighted half-quadratic estimation and adaptive BTV regularization. IEEE Trans. Image Process. (2018)

  10. Farsiu, S., Elad, M., Milanfar, P.: Multiframe demosaicing and super-resolution of color images. IEEE Trans. Image Process. 15(1), 141–159 (2006)

    Article  Google Scholar 

  11. Aguena, M.L., Mascarenhas, N.D.: Multispectral image data fusion using pocs and super-resolution. Comput. Vis. Image Underst. 102(2), 178–187 (2006)

    Article  Google Scholar 

  12. Yang, X., Zhang, Y., Zhou, D., Yang, R.: An improved iterative back projection algorithm based on ringing artifacts suppression. Neurocomputing 162, 171–179 (2015)

    Article  Google Scholar 

  13. Fang, J., Li, J., Shen, Y., Li, H., Li, S.: Super-resolution compressed sensing: an iterative reweighted algorithm for joint parameter learning and sparse signal recovery. IEEE Signal Process. Lett. 21(6), 761–765 (2014)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  15. An, L., Bhanu, B.: Image super-resolution by extreme learning machine. In: 19th IEEE International Conference on Image processing (ICIP), 2012, pp. 2209–2212

  16. 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 

  17. Huang, Y., Wang, W., Wang, L.: Video super-resolution via bidirectional recurrent convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 1015–1028 (2018)

    Article  Google Scholar 

  18. Polatkan, G., Zhou, M., Carin, L., Blei, D., Daubechies, I.: A bayesian nonparametric approach to image super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 37(2), 346–358 (2015)

    Article  Google Scholar 

  19. Li, L., Li, C., Yang, J.: Super-Resolution Restoration and Image Reconstruction for Passive Millimeter Wave Imaging. INTECH Open Access Publisher (2012)

  20. Zhu, S., Li, Y., Chen, J., Li, Y.: Passive millimeter wave image denoising based on adaptive manifolds. Prog. Electromagn. Res. B 57, 63–73 (2014)

    Article  Google Scholar 

  21. Zhang, Q., Shen, X., Xu, L., Jia, J.: Rolling guidance filter. In: European Conference on Computer Vision. Springer, New York, pp. 815–830 (2014)

  22. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, 1998, pp. 839–846

  23. Gastal, E.S., Oliveira, M.M.: Domain transform for edge-aware image and video processing. In: ACM Transactions on Graphics (TOG), vol. 30, no. 4.ACM, 2011, p. 69

  24. He, K., Sun, J., Tang, X.: Guided image filtering. In: European conference on computer vision. Springer, pp. 1–14 (2010)

  25. Singh, P.P., Garg, R.: Fixed point ica based approach for maximizing the non-gaussianity in remote sensing image classification. J. Indian Soc. Remote Sens. 43(4), 851–858 (2015)

    Article  Google Scholar 

  26. Singh, P.P., Garg, R.D.: A hybrid approach for information extraction from high resolution satellite imagery. Int. J. Image Graph. 13(02), 1340007 (2013)

    Article  Google Scholar 

  27. Ogada, E.A., Guo, Z., Wu, B.: An alternative variational framework for image denoising. In: Abstract and Applied Analysis, vol. 2014. Hindawi Publishing Corporation (2014)

  28. Maiseli, B.J., Elisha, O.A., Gao, H.: A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer. EURASIP J. Image Video Process. 2015(1), 22 (2015)

    Article  Google Scholar 

  29. Pipa, D.R., Chan, S.H., Nguyen, T.Q.: Directional decomposition based total variation image restoration. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012, pp. 1558–1562

  30. Patti, A.J., Sezan, M.I., Tekalp, A.M.: Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Trans. Image Process. 6(8), 1064–1076 (1997)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Pham, T.Q., Van Vliet, L.J., Schutte, K.: Robust fusion of irregularly sampled data using adaptive normalized convolution. EURASIP J. Adv. Signal Process. 2006(1), 083268 (2006)

    Article  Google Scholar 

  33. Rudin, L.I., Osher, S.: Total variation based image restoration with free local constraints. In: Image Proceedings. ICIP-94., IEEE International Conference, vol. 1, 1994, pp. 31–35

  34. Köhler, T., Huang, X., Schebesch, F., Aichert, A., Maier, A., Hornegger, J.: Robust multiframe super-resolution employing iteratively re-weighted minimization. IEEE Trans. Comput. Imaging 2(1), 42–58 (2016)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing, China

    Shujin Zhu & Yuehua Li

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  1. Shujin Zhu
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  2. Yuehua Li
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Correspondence to Yuehua Li.

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Zhu, S., Li, Y. Single image super-resolution under multi-frame method. SIViP 13, 331–339 (2019). https://doi.org/10.1007/s11760-018-1361-2

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