Information technology and image forming and processing in output publishing systems

  1. Home
  2. Archive
  3. № 2 (53)
Author(s) Collection number Pages Download abstract Download full text
Havrysh B. M., Levytska H. N., Polishchuk M. B., Tymchenko O. V. № 2 (53) 93-104 Image Image

It is necessary to reduce the noise level, increase the sharpness, reduce the possibility of moire presence, i.e. to improve the quality of the image before printing for automatic processing of images in output publishing systems. It is much easier to carry out a number of methods of images analysis and processing in the transform area using the Fourier transforms in particular. The paper analyzes the method of obtaining the amplitude spectrum of images. The results of its application for images of simple shapes have been presented, the values of amplitude spectrum for blur and detailed images have been considered. The methods of automatic images processing in output publishing systems of laser type have been shown.

Keywords: images, Fourier character, raster structure, amplitude, DFT module, visualization.


  • 1. Ryzhikov, M. B. (2013). Formirovanie i obrabotka izobrazheniy v lazernyih sistemah videniya. Sankt-Peterburg: GUAP (in Russian).
  • 2. Tymchenko, O. V., & Havrysh, B. M. (2015). Analiz metodiv formuvannia rastrovykh tochok v systemakh dodrukarskoi pidhotovky vydan. Komp’iuterni tekhnolohii drukarstva, 2 (34), 89–96 (in Ukrainian).
  • 3. Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images // IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, 1, 34–58 (in English).
  • 4. Chen, Qing, Emil, Petriu, & Xiaoli, Yang (2004). A Comparative Study of Fourier Descriptors and Hu’s Seven Moment Invariants for Image Recognition. CCECE, 0103–0106 (in English).
  • 5. Thawar, Arif, Shaaban, Zyad, Krekor, Lala, & Sami, Baba (2009). Object classification via geometrical, zernike and legendre moments. Journal of Theoretical and Applied Information Technology, Vol. 7, 1, 31–37 (in English).
  • 6. Cruz, V., Crictobal, G., Michaux, T., & Barquin, S. (1989). Invariant image recognition using a multi-network neural model. Electronic Neurocomputers, Proc. Int. Joint Conf. Neural Networks, Vol. 2, 17–21 (in English).
  • 7. Pedro, J. Zufiria, & Javier, Munoz. (1993). Extended backpropogation for invariant pattern recognition neural networks. IJCNN-93, 3, 2097–2100 (in English).
  • 8. Prett, U. (1982). Tsifrovaya obrabotka zobrazheniy. V. 1. (Vols. 1–2). Moscow: Mir (in Russian).