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Image processing for old movies by filters with motion detection

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Języki publikacji
EN
Abstrakty
EN
Old movies suffer from various types of degradation: severe noise, blurred edges of objects (low contrast), scratches, spots, etc. Finding an efficient denoising method is one of the most important and one of the oldest problems in image sequence processing. The crucial thing in image sequences is motion. If the motion is insignificant, then any motion noncompensated method of filtering can be applied. However, if the noise is significant, then this approach gives most often unsatisfactory results. In order to increase the quality of frames, motion compensated filters are usually applied. This is a very time consuming and awkward approach due to serious limitations of optical flow methods. In this paper, a review of various filters with motion detection when applied to the processing of image sequences coming from old movies is presented. These filters are nonlinear and based on the concept of multistage median filtering or mathematical morphology. Some new filters are proposed. The idea of these new filters presented here is to detect moving areas instead of performing full estimation of motion in the sequence and to apply exclusively 2D filters in those regions while applying 3D motion noncompensated filters in static areas, which usually significantly reduces the computational burden.
Rocznik
Strony
481--491
Opis fizyczny
Bibliogr. 13 poz., fot., tab.
Twórcy
  • Institute of Control and Industrial Electronics Warsaw University of Technology ul. Koszykowa 75, 00–662 Warsaw, Poland, S.Skoneczny@isep.pw.edu.pl
Bibliografia
  • [1] Arce G.R. (1991): Multistage order statistic filters for image processing.—IEEE Trans. Signal Process., Vol. 39, No. 5, pp. 1146–1163.
  • [2] Arce G.R. and Foster R. (1989): Detail-preserving rank-order based filters for image processing. —IEEE Trans. Acoust. Speech Signal. Process., Vol. ASSP-37, No. 1, pp. 83–98.
  • [3] Ataman E., Aatre V.K. and Wong K.M. (1981): Some statistical properties of median filters. — IEEE Trans. Acoust. Speech Signal. Process., Vol. ASSP-29, No. 5, pp. 1073–1075.
  • [4] Brailean J.C., Kleihorst R.P., Efstratiadis S., Katsaggelos A.K. and Lagendijk R. (1995): Noise reduction filters for dynamic image sequences: A review. —Proc. IEEE, Vol. 83, No. 9, pp. 1272–1292.
  • [5] Bruni V. and Vitulano D. (2004): Old movies noise reduction via wavelets and Wiener filters. — WSCG, Vol. 12, No. 1–3, pp. 65–72.
  • [6] David H.A. (1980): Order Statistics.—New York: Willey.
  • [7] Huang T.S. (1981): Image Sequence Analysis. — New York: Springer.
  • [8] Kokaram A.C. (1993): Motion Picture Restoration. — Ph.D. thesis, Cambridge University, U.K.
  • [9] Mitra S. and Sicuranza G. (2001): Nonlinear Image Processing. — San Diego: Academic Press.
  • [10] Nieminen A., Heinonen P. and Neuvo Y. (1987): A new class of detail–preserving filters for image processing. — IEEE Trans. Pattern Anal. Mach. Intell., Vol. PAMI-9, No. 1, pp. 74–90.
  • [11] Sezan M. and Lagendijk R. (1993): Motion Analysis and Image Sequence Processing. —Boston: Kluwer.
  • [12] Vega-Riveros J.F. and Jabbour K. (1989): Review of motion analysis techniques. —IEE Proc., Vol. 136, Pt. I, No. 6.
  • [13] Wang X. (1992): Generalized multistage median filters.—IEEE Trans. Image Process., Vol. 1, No. 4, pp. 543–545.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ2-0018-0044
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