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An efficient grey wolf optimization algorithm based extended kalman filtering technique for various image modalities restoration process

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Abstract

The procedure of procurement the original image from the degraded image assumed the knowledge of the debasing factors is called as Image Restoration. The corrupted image will be considered as input and provided to the soft computing method for decreasing the noisy information from the input image in our suggested method. Nonetheless, the established output image obtained from the soft computing method will seem to be blurry with less superiority in the contrast level. This blurred information damages the function of image reconstruction. Thus, into overawed this disadvantage, we deed an Extended Kalman filter method to provide great quality reconstructed the image, in that an optimization algorithm Grey Wolf Optimization (GWO) will be used for a greater reconstructed image. The main contribution of the proposed work is to improve the image quality of reconstruction image by means of optimal Extended Kalman filter. For proving the function of our suggested method, the reconstructed image quality will be associated with the conventional methods. The restoration method was tested with different image modalities such as MRI, CT and also Ultra Sound images of the Human abdomen. The method will be applied to the functioning platform of MATLAB. The implemented restoration technique achieves the maximum Peak Signal to Noise Ratio (PSNR) and structural similarity index (SSIM) value for liver CT image are 35.10db and 0.999 and minimum Mean square error (MSE) and Mean absolute error (MAE) value for liver CT image are 7.90E-05 and 1.1281 respectively.

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References

  1. Ahn S (2004) Convergent algorithms for statistical image reconstruction in emission tomography. Thesis

  2. Alessio A, Kinahan P (2006) PET Image Reconstruction, Second Edition Nuclear Medicine edn. Elsevier, New York, pp 1–22

    Google Scholar 

  3. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(4):996–1002

    Article  Google Scholar 

  4. Dahl J (2010) Per Christian Hansen, Soren Holdt Jensen, and Tobias Lindstrøm Jensen, "Algorithms and software for total variation image reconstruction via first-order methods". Numerical Algorithms 53:67–92

    Article  MathSciNet  Google Scholar 

  5. Denker C, Tritschler A, Lofdahl M (2004) Image Reconstruction. Encyclopedia of Optical Engineering, New York

    Google Scholar 

  6. Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295–307

    Article  Google Scholar 

  7. Erturk H (2011) Evaluation of image reconstruction algorithms for non-destructive characterization of thermal interfaces. Int J Therm Sci 50:906–917

    Article  Google Scholar 

  8. Fessler JA, Rogers L (1995) Resolution properties of regularized image reconstruction methods. Technical Report No. 297

  9. Hiltunen, Prince, Arridge (2009) A combined reconstruction–classification method for diffuse optical tomography. Phys Med Biol 54:6457–6476

    Article  Google Scholar 

  10. Kanakaraj, Kathiravan (2012) Super-resolution image reconstruction using sparse parameter dictionary framework. Sci Res Essays 7(5):586–592

    Article  Google Scholar 

  11. Liebling M (2007) Robust Multiresolution Techniques for Image Reconstruction. Proceedings of Conference on SPIE 6437:64371C-1–64371C-4

    Google Scholar 

  12. Liu Y, Zhang X, Cui J, Wu C, Aghajan H, Zha H (2010) Visual analysis of child-adult interactive behaviors in video sequences. In: Virtual Systems and Multimedia (VSMM), 16th International Conference on, pp. 26–33

  13. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: Pattern Recognition (ICPR), 2012 21st International Conference on, pp. 898–901

  14. Liu J, Huang T-Z, Lv X-G, Huang J (2015) Restoration of blurred color images with impulse noise. Computers and Mathematics with Application 70(6):1255–1265

    Article  MathSciNet  Google Scholar 

  15. Liu J, Huang T-Z, Selesnick IW, Lv X-G, Chen P-Y (2015) Image restoration using total variation with overlapping group sparsity. Inf Sci 295:232–246

    Article  MathSciNet  Google Scholar 

  16. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: Recognizing Complex Activities from Sensor Data. International Joint Conference on Artificial Intelligence, pp. 1617–1623

  17. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  18. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing Complex Activities by a Probabilistic Interval-Based Model. Proceeding of Thirtieth AAAI Conference on Artificial Intelligence 30:1266–1272

    Google Scholar 

  19. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), pp. 201–207

  20. Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimedia Tools and Applications 76(8):10701–10719

    Article  Google Scholar 

  21. Montefusco LB, Lazzaro D, Papi S, Guerrini C (2011) A Fast Compressed Sensing Approach to 3D MR Image Reconstruction. IEEE Trans Med Imaging 30(5):1064–1075

    Article  Google Scholar 

  22. Preotiuc-Pietro D, Liu Y, Hopkins DJ, Ungar L (2017) Beyond binary labels: political ideology prediction of twitter users. In: Annual meeting of the association for computational linguistics

  23. Puetter G, Yahil A (2005) Digital Image Reconstruction: Deblurring and Denoising. Annu Rev Astron Astrophys 43(1):139–194

    Article  Google Scholar 

  24. Rahmati P, Soleimani M, Pulletz S, Frerichs I, Adler A (2012) Level Set based Reconstruction Algorithm for EIT Lung Images: First Clinical Results. Physiol Meas 33(5):1–14

    Article  Google Scholar 

  25. Saharan R, Singh CV (2011) Reassembly of 2D Fragments in Image Reconstruction. Int J Comput Appl 19(5):41–45

    Google Scholar 

  26. Sakthidasan K, Velmurugan Nagappan N (2016) Noise-free image restoration using a hybrid filter with adaptive genetic algorithm. Comput Electr Eng 1:1–11

    Google Scholar 

  27. Salmon BP, Kleynhans W, van denbergh F, Olivier JC, Grobler TL, Wessels KJ (2013) Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(3):1079–1085

    Article  Google Scholar 

  28. Schweiger M, Arridge SR (1999) Optical tomographic reconstruction in a complex head model using a priori region boundary information. Phys Med Biol 44:2703–2721

    Article  Google Scholar 

  29. Schweiger M, Arridge SR, Nissila I (2005) Gauss-Newton method for image reconstruction in diffuse optical tomography. Phys Med Biol 50:2365–2386

    Article  Google Scholar 

  30. Song J, Yang Y, Huang Z, Shen HT, Luo J (2013) Effective multiple feature hashing for large-scale near-duplicate video retrieval. IEEE Transactions on Multimedia 15(8):1997–2008

    Article  Google Scholar 

  31. Song J, Gao L, Nie F, Shen HT, Yan Y, Sebe N (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans Image Process 25(11):4999–5011

    Article  MathSciNet  Google Scholar 

  32. Wang X, Gao L, Song J, Shen H (2017) Beyond frame-level CNN: saliency-aware 3-D CNN with LSTM for video action recognition. IEEE Signal Processing Letters 24(4):510–514

    Article  Google Scholar 

  33. Williams BA, Long DG (2011) Reconstruction from Aperture-Filtered Samples With Application to Scatterometer Image Reconstruction. IEEE Trans Geosci Remote Sens 49(5):1663–1676

    Article  Google Scholar 

  34. Yu F (2000) Statistical Methods for Transmission Image Reconstruction with Nonlocal Edge-Preserving Regularization. Thesis

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Correspondence to B. Baron Sam.

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Baron Sam, B., Lenin Fred, A. An efficient grey wolf optimization algorithm based extended kalman filtering technique for various image modalities restoration process. Multimed Tools Appl 77, 30205–30232 (2018). https://doi.org/10.1007/s11042-018-6088-0

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  • DOI: https://doi.org/10.1007/s11042-018-6088-0

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