BibTex RIS Kaynak Göster

Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması

Yıl 2018, Cilt: 8 Sayı: 2, 561 - 569, 01.06.2018

Öz

Bu makalenin amacı, yapay sinir ağları yaklaşımını sayısal holografide hologramdan geri elde edilen üç boyutlu görüntünün iyileştirilmesi için kullanmaktır. Gerchberg-Saxton algoritmasına dayalı yapay sinir ağları metodu, gürültünün azaltılması ve bu görüntünün parlaklığının arttırılması için uygulanmıştır. Önerilen metodun sonuçları, bağıl hata ile sunulmuştur. Ayrıca, bu bağıl hata şekilleri, MATLAB yapay sinir ağları araç kutusu ile elde edilen hata histogramları ile desteklenmiştir

Kaynakça

  • Avidor, G., Gur, E. 2010. An adaptive algorithm for phase retrieval from high intensity images. International Conference on Image Processing Theory Tools and Applications. pp. 225-228, Paris.
  • Duda, RO., Hart, PE., Stork, DG. 2000. Pattern Classification. 2 nd Ed, John Wiley, New York, USA, 680 pp.
  • Dyomin, VV., Kamenev, DV., Olshukov, AS. 2014. Methods for image enhancement and accuracy increase in the digital holography of particles. Oceans 2014. pp. 1-5, Taiwan.
  • Farnood Ahmadi, F., Valadan Zoej, MJ., Ebadi, H., Mokhtarzade, M. 2008. The application of neural networks, image processing and cad-based environments facilities in automatic road extraction and vectorization from high resolution satellite images. Int. Arch. Photogramm. Rem. S. Spat. Inf. Sci., 37(1): 585-592.
  • Gerchberg, RW., Saxton, WO. 1972. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik, 35(2):237-246.
  • Avidor, G., Gur, E. 2010. An adaptive algorithm for phase retrieval from high intensity images. International Conference on Image Processing Theory Tools and Applications. pp. 225-228, Paris.
  • Duda, RO., Hart, PE., Stork, DG. 2000. Pattern Classification. 2 nd Ed, John Wiley, New York, USA, 680 pp.
  • Dyomin, VV., Kamenev, DV., Olshukov, AS. 2014. Methods for image enhancement and accuracy increase in the digital holography of particles. Oceans 2014. pp. 1-5, Taiwan.
  • Farnood Ahmadi, F., Valadan Zoej, MJ., Ebadi, H., Mokhtarzade, M. 2008. The application of neural networks, image processing and cad-based environments facilities in automatic road extraction and vectorization from high resolution satellite images. Int. Arch. Photogramm. Rem. S. Spat. Inf. Sci., 37(1): 585-592.
  • Gerchberg, RW., Saxton, WO. 1972. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik, 35(2):237-246.
  • Hect-Nielsen, R. 1989. Theory of the back propagation neural network. International 1989 Joint Conference on Neural Networks. pp. 593-605. Washington, DC, USA.
  • Hornik, K., Stinchcombe, M., White, H. 1989. Multilayer feed forward networks are universal approximators. Neural Networks. 2(1):359-366.
  • Isa, IS., Saad, Z., Omar, S., Osman, MK., Ahmad, KA., Mat Sakim, HA. 2010. Suitable MLP network activation functions for breast cancer and thyroid disease detection. Second International Conference on Computational Intelligence, Modelling and Simulation. pp. 39-44. Tuban, Indonesia.
  • Latifoglu, F. 2013. A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: an ultrasound image application. Comput. Meth. Prog. Bio., 111(3):561-569.
  • Latychevskaia, T., Fink, HW. 2013. Resolution enhancement in digital holography by self-extrapolation of holograms. Opt. Express, 21(6):7726-7733.
  • Liu, N., Li, W., Zhao, D. 2016. Enhancement of low-quality reconstructed digital hologram images based on frequency extrapolation of large objects under the diffraction limit. Opt. Rev., 23(3):448-459.
  • Maity, A., Pattanaik, A., Sagnika, S., Pani, S. 2015. A comparative study on approaches to speckle noise reduction in images. International Conference on Computational Intelligence & Networks (CINE). pp. 148-155. Bhubaneshwar, India.
  • Nakamura, T., Nitta, K., Matoba, O. 2007. Iterative algorithm of phase determination in digital holography for real-time recording of real objects. Appl. Opt., 46(28):6849-6853.
  • Pinjare, SL, Kumar, MA. 2012. Implementation of neural network back propagation training algorithm on FPGA. Int. J. Comput. Appl., 52(6):1-7.
  • Saikia, T., Sarma, KK. 2014. Multilevel-DWT based image de-noising using feed forward artificial neural network. International Conference on Signal Processing and Integrated Networks (SPIN), pp. 791-794. Noida, Delhi-NCR, India.
  • Schenider, B., Dambre, J., Bienstman, P. 2016. Fast particle characterization using digital holography and neural networks. Appl. Opt., 55(1):133-139.
  • Shechtman, Y., Eldar, YC., Cohen, O., Chapman, HN., Miao, J., Segev, M. 2015. Phase retrieval with application to optical imaging: A contemporary overview. IEEE Signal Proc. Mag., 32(3):87-109.
  • Takeda, M., Mutoh, K. 1983. Fourier transform profilometry for the automatic of 3-D object shapes. Appl. Opt., 22(24):3977- 3982.
  • Ustabaş Kaya, G., Saraç, Z. 2017. Comparing of phase shifting method and one-dimensional continuous wavelet transform method for reconstruction using phase-only information. Turk. J. Electr. Eng. Co., 25(2):1587-1597.
  • Verma, R., Ali, J. 2013. A comparative study of various types of image noise and efficient noise removal techniques. Int. J. Adv. Res. Com. Sci. Softw. Eng., 3(10):617-622.
  • Zhang, S., Zhou, J. 2014. Image resolution enhancement in digital holography. X. International Conference on Natural Computation (ICNC), pp. 942-946. Xiamen, China.

Usage of artificial neural networks method for image enhancement of reconstructed image in digital holography

Yıl 2018, Cilt: 8 Sayı: 2, 561 - 569, 01.06.2018

Öz

The aim of paper is to use an artificial neural network approach for enhancement of three dimensional image reconstructed in digital holography. An artificial neural network method based on Gerchberg-Saxton algorithm is implemented to reduce the noise and increase the brightness of this image. The results of proposed method have been presented by a relative error. In addition, these relative error figures are supported with error histogram obtained from MATLAB neural network fitting toolbox.

Kaynakça

  • Avidor, G., Gur, E. 2010. An adaptive algorithm for phase retrieval from high intensity images. International Conference on Image Processing Theory Tools and Applications. pp. 225-228, Paris.
  • Duda, RO., Hart, PE., Stork, DG. 2000. Pattern Classification. 2 nd Ed, John Wiley, New York, USA, 680 pp.
  • Dyomin, VV., Kamenev, DV., Olshukov, AS. 2014. Methods for image enhancement and accuracy increase in the digital holography of particles. Oceans 2014. pp. 1-5, Taiwan.
  • Farnood Ahmadi, F., Valadan Zoej, MJ., Ebadi, H., Mokhtarzade, M. 2008. The application of neural networks, image processing and cad-based environments facilities in automatic road extraction and vectorization from high resolution satellite images. Int. Arch. Photogramm. Rem. S. Spat. Inf. Sci., 37(1): 585-592.
  • Gerchberg, RW., Saxton, WO. 1972. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik, 35(2):237-246.
  • Avidor, G., Gur, E. 2010. An adaptive algorithm for phase retrieval from high intensity images. International Conference on Image Processing Theory Tools and Applications. pp. 225-228, Paris.
  • Duda, RO., Hart, PE., Stork, DG. 2000. Pattern Classification. 2 nd Ed, John Wiley, New York, USA, 680 pp.
  • Dyomin, VV., Kamenev, DV., Olshukov, AS. 2014. Methods for image enhancement and accuracy increase in the digital holography of particles. Oceans 2014. pp. 1-5, Taiwan.
  • Farnood Ahmadi, F., Valadan Zoej, MJ., Ebadi, H., Mokhtarzade, M. 2008. The application of neural networks, image processing and cad-based environments facilities in automatic road extraction and vectorization from high resolution satellite images. Int. Arch. Photogramm. Rem. S. Spat. Inf. Sci., 37(1): 585-592.
  • Gerchberg, RW., Saxton, WO. 1972. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik, 35(2):237-246.
  • Hect-Nielsen, R. 1989. Theory of the back propagation neural network. International 1989 Joint Conference on Neural Networks. pp. 593-605. Washington, DC, USA.
  • Hornik, K., Stinchcombe, M., White, H. 1989. Multilayer feed forward networks are universal approximators. Neural Networks. 2(1):359-366.
  • Isa, IS., Saad, Z., Omar, S., Osman, MK., Ahmad, KA., Mat Sakim, HA. 2010. Suitable MLP network activation functions for breast cancer and thyroid disease detection. Second International Conference on Computational Intelligence, Modelling and Simulation. pp. 39-44. Tuban, Indonesia.
  • Latifoglu, F. 2013. A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: an ultrasound image application. Comput. Meth. Prog. Bio., 111(3):561-569.
  • Latychevskaia, T., Fink, HW. 2013. Resolution enhancement in digital holography by self-extrapolation of holograms. Opt. Express, 21(6):7726-7733.
  • Liu, N., Li, W., Zhao, D. 2016. Enhancement of low-quality reconstructed digital hologram images based on frequency extrapolation of large objects under the diffraction limit. Opt. Rev., 23(3):448-459.
  • Maity, A., Pattanaik, A., Sagnika, S., Pani, S. 2015. A comparative study on approaches to speckle noise reduction in images. International Conference on Computational Intelligence & Networks (CINE). pp. 148-155. Bhubaneshwar, India.
  • Nakamura, T., Nitta, K., Matoba, O. 2007. Iterative algorithm of phase determination in digital holography for real-time recording of real objects. Appl. Opt., 46(28):6849-6853.
  • Pinjare, SL, Kumar, MA. 2012. Implementation of neural network back propagation training algorithm on FPGA. Int. J. Comput. Appl., 52(6):1-7.
  • Saikia, T., Sarma, KK. 2014. Multilevel-DWT based image de-noising using feed forward artificial neural network. International Conference on Signal Processing and Integrated Networks (SPIN), pp. 791-794. Noida, Delhi-NCR, India.
  • Schenider, B., Dambre, J., Bienstman, P. 2016. Fast particle characterization using digital holography and neural networks. Appl. Opt., 55(1):133-139.
  • Shechtman, Y., Eldar, YC., Cohen, O., Chapman, HN., Miao, J., Segev, M. 2015. Phase retrieval with application to optical imaging: A contemporary overview. IEEE Signal Proc. Mag., 32(3):87-109.
  • Takeda, M., Mutoh, K. 1983. Fourier transform profilometry for the automatic of 3-D object shapes. Appl. Opt., 22(24):3977- 3982.
  • Ustabaş Kaya, G., Saraç, Z. 2017. Comparing of phase shifting method and one-dimensional continuous wavelet transform method for reconstruction using phase-only information. Turk. J. Electr. Eng. Co., 25(2):1587-1597.
  • Verma, R., Ali, J. 2013. A comparative study of various types of image noise and efficient noise removal techniques. Int. J. Adv. Res. Com. Sci. Softw. Eng., 3(10):617-622.
  • Zhang, S., Zhou, J. 2014. Image resolution enhancement in digital holography. X. International Conference on Natural Computation (ICNC), pp. 942-946. Xiamen, China.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

Gülhan Ustabaş Kaya Bu kişi benim

Zehra Saraç Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 8 Sayı: 2

Kaynak Göster

APA Ustabaş Kaya, G., & Saraç, Z. (2018). Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması. Karaelmas Fen Ve Mühendislik Dergisi, 8(2), 561-569.
AMA Ustabaş Kaya G, Saraç Z. Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması. Karaelmas Fen ve Mühendislik Dergisi. Haziran 2018;8(2):561-569.
Chicago Ustabaş Kaya, Gülhan, ve Zehra Saraç. “Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması”. Karaelmas Fen Ve Mühendislik Dergisi 8, sy. 2 (Haziran 2018): 561-69.
EndNote Ustabaş Kaya G, Saraç Z (01 Haziran 2018) Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması. Karaelmas Fen ve Mühendislik Dergisi 8 2 561–569.
IEEE G. Ustabaş Kaya ve Z. Saraç, “Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması”, Karaelmas Fen ve Mühendislik Dergisi, c. 8, sy. 2, ss. 561–569, 2018.
ISNAD Ustabaş Kaya, Gülhan - Saraç, Zehra. “Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması”. Karaelmas Fen ve Mühendislik Dergisi 8/2 (Haziran 2018), 561-569.
JAMA Ustabaş Kaya G, Saraç Z. Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması. Karaelmas Fen ve Mühendislik Dergisi. 2018;8:561–569.
MLA Ustabaş Kaya, Gülhan ve Zehra Saraç. “Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması”. Karaelmas Fen Ve Mühendislik Dergisi, c. 8, sy. 2, 2018, ss. 561-9.
Vancouver Ustabaş Kaya G, Saraç Z. Sayısal Holografide Geri Elde Edilen Görüntünün İyileştirilmesi İçin Yapay Sinir Ağları Metodunun Kullanılması. Karaelmas Fen ve Mühendislik Dergisi. 2018;8(2):561-9.