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An edge preserving IBP based super resolution image reconstruction using P-spline and MuCSO-QPSO algorithm

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Abstract

Super resolution (SR) reconstruction based on iterative back projection (IBP) is a widely used image reconstruction method. IBP approach is easy to implement and allows easy inclusion of the spatial domain with low computational complexity. However, local minima trapping; slow rate of convergence; sensitive to the initial guess; prone to ringing and jaggy artifacts are some major bottlenecks which restrict its performance. The present paper aims to enhance the performance of IBP based SR reconstruction (IBP-SRR) of image by exploring an effective method. The proposed method has fast convergence rate, a global optimal solution, capability to lessen the effect of artifacts and a noble generalization performance. In the present work, P-spline interpolation scheme imposes additional penalty in the inherently smooth B-spline interpolation process to provide a proper initial guess. An adaptive edge regularization technique is used in the constraint optimization of the reconstruction problem to minimize the effect of ringing artifacts. Finally, the overall reconstruction error of the reconstruction system is optimized using a hybrid meta-heuristic optimization technique. The optimization algorithm hybridizes the notion of Cuckoo search optimization (CSO) algorithm with a mutation operator (MuCSO) and the quantum behaved particle swarm optimization (QPSO). The MuCSO-QPSO algorithm is compared with other significant optimization algorithms such as GA, PSO, QPSO, CSO, MuCSO and found to be outperforming. Experimental results demonstrate the superiority of the proposed edge preserving IBP-SRR method in terms of enhanced spatial resolution, and more detail reconstruction.

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References

  • Aguena ML, Mascarenhas ND (2011) Generalization of iterative restoration techniques for super-resolution. In: Graphics, patterns and images (Sibgrapi), 2011 24th SIB-GRAPI Conference on, IEEE, pp 258–265

  • Bahy RM, Salama GI, Mahmoud TA (2014) Adaptive regularization-based super resolution reconstruction technique for multi-focus low-resolution images. Signal Process 103:155–167

    Article  Google Scholar 

  • Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346

    Article  Google Scholar 

  • Clement GT, Huttunen J, Hynynen K (2005) Super resolution ultrasound imaging using back-projected reconstruction. J Acoust Soc Am 118(6):3953–3960

    Article  Google Scholar 

  • Clerc M, Kennedy J (2002) The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  • Dong W, Zhang D, Shi G, Wu X (2009) Nonlocal back-projection for adaptive image enlargement. Image Processing (ICIP) 16th IEEE International Conference, pp 349–352

  • Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. Image Process IEEE Trans 20(7):1838–1857

    Article  MathSciNet  Google Scholar 

  • Farsiu S, Robinson D, Elad M, Milanfar P (2004) Advances and challenges in super-resolution. Int J Imaging Syst Technol 14(2):47–57

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization IEEE International Conference on Neural Networks Perth, 1995. In: IEEE International Conference on Neural Networks Perth, pp 1942–1948

  • Lertrattanapanich S, Bose NK (2002) High resolution image formation from low resolution frames using Delaunay triangulation. Image Process IEEE Trans 11(12):1427–1441

    Article  MathSciNet  Google Scholar 

  • Li X, Orchard T (2001) New edge-directed interpolation. Image Process IEEE Trans 10(10):1521–1527

    Article  Google Scholar 

  • Li X, Hu Y, Gao X, Tao D, Ning B (2010) A multi-frame image super-resolution method. Signal Process 90(2):405–414

    Article  MATH  Google Scholar 

  • Liang X, Gan Z (2011) Improved non-local iterative back-projection method for image super-resolution. Image and Graphics (ICIG), 2011 Sixth International Conference, pp 176–181

  • Makwana RR, Mehta ND (2013) Single image super-resolution via iterative back projection based canny edge detection and a gabor filter prior. Int J Soft Comput Eng 3(1):2231–2307

    Google Scholar 

  • Marx BD (2010) P-spline varying coefficient models for complex data. In: Statistical modelling and regression structures, Springer, pp 19–43

  • Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2004) Perceptual blur and ringing metrics: application to jpeg2000. Signal Process Image Commun 19(2):163–172

    Article  Google Scholar 

  • Nayak R, Monalisa S, Patra D (2013). Spatial super resolution based image reconstruction using HIBP. India Conference (INDICON), 2013 Annual IEEE, pp 1–6

  • Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. Signal Process Mag IEEE 20(3):21–36

    Article  Google Scholar 

  • Patti AJ, Altunbasak Y (2001) Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants. IEEE Trans Image Process 10(1):179–186

    Article  Google Scholar 

  • Periaswamy S, Farid H (2003) Elastic registration in the presence of intensity variations. Med Imaging IEEE Trans 22(7):865–874

    Article  MATH  Google Scholar 

  • Pradhan S, Patra D (2013) P-spline based nonrigid brain mr image registration using regional mutual information. In: India Conference (INDICON), 2013 Annual IEEE, IEEE, pp 1–5

  • Pradhan S, Patra D (2015) RMI based non-rigid image registration using BF-QPSO optimization and P-spline. AEU-Int J Electron Commun 69(3):609–621

    Article  Google Scholar 

  • Purkait P, Chanda B (2013) Morphologic gain-controlled regularization for edge-preserving super-resolution image reconstruction. Signal Image Video Process 7(5):925–938

    Article  Google Scholar 

  • Rani A, Malek A, Chin S, Wahab A (2014) Modified and hybrid cuckoo search algorithms via weighted-sum multiobjective optimization for symmetric linear array geometry synthesis. Int J Adv Res Comput Commun Eng 3(5):6774–6781

    Google Scholar 

  • Rubert C, Fonseca L, Velho L (2005) Learning based super-resolution using YUV model for remote sensing images. Proceedings of WTDCGPI

  • Song H, He X, Chen W, Sun Y (2010) An improved iterative back-projection algorithm for video super-resolution reconstruction. In: Photonics and optoelectronic (SOPO), 2010 Symposium on, IEEE, pp 1–4

  • Stark H, Oskoui P (1989) High-resolution image recovery from image-plane arrays, using convex projections. JOSA A 6(11):1715–1726

    Article  Google Scholar 

  • Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior, IEEE proceedings of congress on evolutionary computation, pp 325–331

  • Sun J, Fang W, Palade V, Wu X, Xu W (2011) Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point. Appl Math Comput 218(7):3763–3775

    MATH  Google Scholar 

  • Valian E, Mohanna S, Tavakoli S (2011) Improved cuckoo search algorithm for global optimization. Int J Commun Inf Technol 1(1):31–44

    Google Scholar 

  • Wang Z, Bovik C (2002) A universal image quality index. Signal Process Lett IEEE 9(3):81–84

    Article  Google Scholar 

  • Wang Z, Bovik C, Sheikh R, Simoncelli P (2004) Image quality assessment: from error visibility to structural similarity. Image Process IEEE Trans 13(4):600–612

    Article  Google Scholar 

  • Wong A, Bishop W(2007) Adaptive large scale artifact reduction in edge-based image super-resolution. In: SIP, Cite-seer, pp 225–229

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. Nature & biologically inspired computing, 2009 NaBIC 2009 World Congress, pp 210–214

  • Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Yu-qian Z, Wei-hua G, Zhen-cheng C, Jing-tian T, Ling-Yun L (2006) Medical images edge detection based on mathematical morphology. Engineering in medicine and biology society, IEEE-EMBS 27th Annual International Conference, pp 6492–6495

  • Zhang L, Zhang D, Mou X (2011) FSIM: a feature similarity index for image quality assessment. Image Process IEEE Trans 20(8):2378–2386

    Article  MathSciNet  Google Scholar 

  • Zhang Y, Tao M, Yang K, Deng Z (2015) Video super resolution reconstruction using iterative back projection with critical-point lters based image matching. Adv Multimed 2015:4

    Article  Google Scholar 

Download references

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Correspondence to Rajashree Nayak.

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Nayak, R., Patra, D. An edge preserving IBP based super resolution image reconstruction using P-spline and MuCSO-QPSO algorithm. Microsyst Technol 23, 553–569 (2017). https://doi.org/10.1007/s00542-016-2972-6

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