Usage of segmentation for noise elimination in reconstructed images in digital holographic interferometry

In this paper, we propose to enhance the image in digital holography by using an arti cial neural network and an iterative algorithm with Nakamura's approach based on segmentation. It is well known that reconstructed three- dimensional (3D) images suffer from noise in digital holography. In addition, obtaining 3D reconstructed images takes a long time due to large pixel numbers in reconstructed images and lack of memory in the system. The segmentation process is an application that overcomes these problems. Therefore, we focus on the implementation of segmentation for image enhancement. In addition, the results of the segmentation process for both methods are compared in terms of image enhancement. Later, the relative errors are calculated.

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[1] Cronan JJ. Ultrasound: is there a future in diagnostic imaging? Journal of the American College of Radiology 2006; 3: 645-646.
[2] Maini R, Aggarwal H. A comprehensive review of image enhancement technique. J Comput 2010; 2: 8-13.
[3] Podoleanu AG. Optical coherence tomography. Brit J Radiol 2005; 78: 976-988.
[4] Sarode MV, Deshmukh PR. Reduction of speckle noise and image enhancement of images using ltering technique. International Journal of Advancements in Technology 2011; 2: 30-38.
[5] Abdelsalam DG, Kim D. Coherent noise suppression in digital holography based on at elding with apodized apertures. Opt Express 2011; 19: 17951-17959.
[6] Kaur S. Noise types and various removal techniques. International Journal of Advanced Research in Electronics and Communication Engineering 2015; 4: 226-230.
[7] Kaisar S, Rijwan S, Al Mahmud J, Rahman MM. Salt and pepper noise detection and removal by tolerance based selective arithmetic mean ltering technique for image restoration. International Journal of Computer Science and Network Security 2008; 8: 271-278.
[8] Mythili C, Kavitha V. Efficient technique for color image noise reduction. Research Bulletin of Jordan 2011; 2: 41-44.
[9] Cai X, Wang H. The in uence of hologram aperture on speckle noise in the reconstructed image of digital holography and its reduction. Optics Communication 2008; 281: 232-237.
[10] Cai X. Reduction of speckle noise in the reconstructed image of digital holography. Optik 2010; 121: 394-399.
[11] Gerchberg RW, Saxton WO. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik 1972; 35: 237-246.
[12] Rong L, Xiao W, Pan F, Liu S, Li R. Speckle noise reduction in digital holography by use of multiple polarization holograms. Chin Opt Lett 2010; 8: 653-655.
[13] Nakamura T, Nitta K, Matoba O. Iterative algorithm of phase determination in digital holography for real-time recording of real objects. Appl Optics 2007; 46: 6849-6853.
[14] Latifoglu F. A novel approach to speckle noise ltering based on arti cial bee colony algorithm: an ultrasound image application. Comput Meth Prog Bio 2013; 111: 561-569.
[15] Farnood Ahmadi F, Valadan Zoej MJ, Ebadi H, Mokhtarzade M. The application of neural networks, image processing and cad-based environments facilities in automatic road extraction and vectorization from high resolution satellite images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2008; 37: 585-592.
[16] Zurada JM. Introduction To Arti cial Neural Systems. Bristol, UK: West Publication Company, 1992.
[17] Saikia T, Sarma KK. Multilevel-DWT based image de-noising using feed forward arti cial neural network. In: IEEE 2014 International Conference on Signal Processing and Integrated Networks; February 2014; Delhi, India. New York, NY, USA: IEEE. pp. 791-794.
[18] Nayak R, Jain L, Ting B. Arti cial neural networks in biomedical engineering: a review. In: Proceedings of the 1st Asian-Paci c Congress on Computational Mechanics; 2001. Amsterdam, the Netherlands: Elsevier Science Limited. pp. 887-892.
[19] Chandhok C. A novel approach to image segmentation using arti cial neural networks and K-means clustering. International Journal of Engineering Research and Applications 2012; 2: 274-279.
[20] Agravat H, Rathod K. Classi cation of different segmentation methods for handwritten words. Research Hub- International Multidisciplinary Research Journal 2015; 2: 1-4.
[21] Duda RO, Hart PE, Stork DG. Pattern Classi cation. 2nd ed. New York, NY, USA: Wiley Publishing, 2000.
[22] Isa IS, Saad Z, Omari S. Suitable MLP network activation functions for breast cancer and thyroid disease detection. In: IEEE 2010 Second International Conference on Computational Intelligence, Modelling and Simulation; 28{30 September 2010; Tuban, Indonesia. New York, NY, USA: IEEE. pp. 39-44.
[23] Chang KT. Computation for Bilinear Interpolation. Introduction to Geographic Information Systems. 5th ed. New York, NY, USA: McGraw-Hill, 2009.