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SETSCI - Volume 1 (2017)
ISMSIT2017 - International Symposium on Multidisciplinary Studies and Innovative Technologies, Tokat, Turkey, Dec 02, 2017

Image enhancement by using Hybrid methods based on Artificial Neural Networks
Gülhan Ustabaş Kaya1*, Zehra Saraç2
1Bulent Ecevit University, Zonguldak, Turkey
2Bulent Ecevit University, Zonguldak, Turkey
* Corresponding author: gulhan.ustabas@beun.edu.tr
Published Date: 2017-12-08   |   Page (s): 133-135   |    546     9
ABSTRACT In this study, the elimination of zero diffraction order and twin image from three dimensional reconstructed images in digital holography and the image enhancement are desired to achieve in a short time by using hybrid methods. In addition, the brightness of the reconstructed three dimensional image is aimed to increase by using hybrid methods based on artificial neural network for the first time. To calculate the accurate phase of the hologram using in Gerchberg-Saxton algorithm (GSA), the Fourier transform algorithm and 1- dimensional continuous wavelet transform (1D-CWT) are used. These methods are expressed as hybrid methods. The usage of 1D-CWT for using in GSA is a first attempt. The intended purpose of using 1D-CWT is to reduce the twin image and zero diffraction order with performing minimum iteration numbers. The ratio of image enhancement is given by calculating the normalized root mean square values.  
KEYWORDS image enhancement, digital holography, hybrid method, Artificial neural network, Gerchberg-Saxton algorithm, 1- dimensional continuous wavelet transform
REFERENCES [1] J. Zhong and J. Weng, Reconstruction of digital hologram by use of the wavelet transform, in Holography, Research and Technologies, J. Rosen, Ed. InTech, 2011.

[2] K. Khare, P.T.Samsheer Ali and J. Joseph, “Single shot high resolution digital holography,” Optics Express, vol. 21, pp. 2581-2591, 2013.

[3] K. Creath, “ Phase-measurement interferometry techniques,” Progress in Optics, vol. 26, pp.349–393, 1988.

[4] M. Takeda and K. Mutoh, “Fourier transform profilometry for the automatic measurement of 3-D objects shapes,” Applied Optics, vol. 22, pp. 3977-3982, 1983.

[5] Jaideva C. Goswami and Andrew K. Chan, Fundamentals of Wavelets: Theory, algorithms, and applications,2nd ed., Wiley, 2011.

[6] M. Bahich,, M. Afifi and E. Barj, “Optical phase extraction algorithm based on the continuous wavelet and the hilbert transforms,” Journal of Computing, vol. 2, pp. 1-5, 2010.

[7] D. Onal Tayyar, Z. Saraç and N. F. Ecevit, “Real-time optical recontruction of the diffused 3D object using phase information calculated by continuous wavelet transform,” Optics Communications, vol. 284, pp. 5460-5465, 2011.

[8] T. Nakamura, K. Nitta and O. Matoba, “Iterative algorithm of phase determination in digital holography for real-time recording of real objects,” Applied Optics, vol. 46, pp. 6849-6853, 2007.

[9] F. Latifoglu, “A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: an ultrasound image application,” Journal Computer Methods and Programs in Biomedicine, vol. 111, pp. 561-569, 2013.

[10] R. W. Gerchberg and W. O. Saxton, “A Practical algorithm for the determination of phase from image and diffraction plane pictures,” Optik, vol. 35, pp. 237-246, 1972.

[11] M. Liebling, T.F. Bernhard, A. H. Bachman, L. Froehly, T. Lasser, and M. Unser, “Continuous wavelet transform ridge extraction for spectral interferometry imaging,“ in Proc. SPIE 5690, Coherence Domain Optical Methods and Optical Coherence Tomography in Biomedicine IX. 2005.

[12] A. Grossman and J. Morlet, Decomposition of functions into wavelets of constant shape and related transforms, Mathematics and physics, lectures on recent results, Ed.: L. Streit, World Scientific Publishing, Singapore. 1985, vol. 1.

[13] F. S. Gharehchopogh, M. Molany and F. D. Mokri, “Using artificial neural network in diagnosis of thyroid disease: a case study,” International Journal on Computational Sciences & Applications (IJCSA), vol. 3, pp. 49-61, 2013.

[14] V. Sarasvathi and Dr. A. Santhakumaran, “Towards artificial neural network model to diagnose thyroid problems, ” Global Journal of Computing Science & Technology, vol. 11, pp. 52-55, 2011.

[15] L. L. Guan, R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, New York: John Wiley& amp; Sons, 2007.


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