Multimedia Tools and Applications

, Volume 71, Issue 2, pp 451–467 | Cite as

An automatic video scratch removal based on Thiele type continued fraction

  • Xing HuoEmail author
  • Jieqing Tan
  • Lei He
  • Min Hu


Old age, repeat play and improper preservation always deteriorate the film, and dust and mechanical operations produce artifacts like scratches and blotches. Many researches carried out to repair the damaged digital videos and video inpainting gradually becomes an important topic in digital image process ing. Challenges in scratched video inpainting are automatic detection of scratches and restoration of damaged part. This paper presents an automatic scratch detec tion method as well as a novel scratch removal approach. Stationary wavelet transform (SWT) which shows excellent performance in keeping translation-invariant is introduced to automatically detect the scratches, this strategy makes the scratches’ detection more accurate. At the heart of our method is a new nonlinear interpolation method based on continued fraction in which Thiele-type continued fraction is used to interpolate surrounding known pixels for repairing the damaged part. Algorithm presented in this paper also utilizes both spatial and temporal information of the scratched video during the restoration stage. Experimental results show that our scheme not only obtains more accurate detection of scratches, but also gives better video quality.


Video inpainting Scratch detection Continued fraction Non-linear interpolation 



Partial differential equation


Total variation


Curvature-driven diffusion


Over-complete wavelet expansion


Stationary wavelet transform


Global bi-directional motion compensation frame interpolation


Motion vectors


Continued fraction



The authors would like to thank Prelinger Archives ( for archive film material. We sincerely appreciate the financial support of National Natural Science Foundation of China and we are also grateful to editor and reviewers for their constructive comments and suggestions.


  1. 1.
    Bao P, Zhang L (2003) Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Trans Med Imaging 22:1089–1099. doi: 10.1109/TM I. 2003.816958 CrossRefGoogle Scholar
  2. 2.
    Bertalmio M, Sapiro G, Ballester C, Ballester C (2000) Image inpainting. Proc. SIGGRAPH’00 pp 417–424. doi: 10.1145/344779.344972
  3. 3.
    Chambolle A (2004) An algorithm for total variation minimization and applications. J Math Imaging Vis 20:89–97CrossRefMathSciNetGoogle Scholar
  4. 4.
    Chan TF, Shen J (2001) Non-texture inpainting by curvature driven diffusions. J Vis Commun Image Represent 12:436–449. doi: 10.1006/jvci.2001.0487 CrossRefGoogle Scholar
  5. 5.
    Chan TF, Shen J (2002) Mathematical models of local non-texture inpaintings. SIAM J Appl Math 62:1019–1043. doi: 10.1137/S0036139900368844 CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Dubey N, Agrawal V, Mohapatra S (2009) A generalized wavelet expansion-based algorithm for line scratches detection in old colored or grey videos and static images. Accessed Nov 2009
  7. 7.
    Graves-Morris PR (1981) Efficient reliable rational interpolation. Padé approximation and its applications. Lect Notes Math 888:28–63. doi: 10.1007/BFb0095575 CrossRefMathSciNetGoogle Scholar
  8. 8.
    Güllü MK, Urhan O, Ertürk S (2006) Scratch detection via temporal coherency analysis and removal using edge priority based interpolation. Proc. ISCAS 2006 pp 92–96. doi: 10.1109/ISCAS.2006.1693652
  9. 9.
    He SQ, Xing CJ, Zhao PM (2011) Global bi-directional motion compensation frame interpolation algorithm. Multimed Tools Appl 52:19–31. doi: 10.1007/s11042-009-0450-1 CrossRefGoogle Scholar
  10. 10.
    Joyeux L, Besserer B, Boukir S (2002) Tracking and map reconstruction of line scratches in degraded motion pictures. Mach Vis Appl 13:119–128. doi: 10.1007/s001380100067 CrossRefGoogle Scholar
  11. 11.
    Joyeux L, Buisson O, Besserer B, Boukir S (1999) Detection and removal of line scratches in motion picture films. Proc. CVPR’99 pp 548–553. doi:  10.1109/CVPR.1999.786991
  12. 12.
    Joyeux L, Buisson O, Besserer B, Boukir S (2001) Reconstruction of degraded image sequences application to film restoration. Image Vision Comput 19:503–516. doi: 10.1016/S0262-8856(00)00091-3 CrossRefGoogle Scholar
  13. 13.
    Milukova O, Kober V, Karnaukhov V, Ovseyevich IA (2010) Restoration of blurred images with conditional total variation method. Pattern Recogn Image Anal 20:179–184CrossRefGoogle Scholar
  14. 14.
    Nie SL, Zhang HY, Zhang LP, Fan Y, Brost V (2010) Vertical scratches detection based on edge detection for old film. Proc. IIS 2010 pp 257–260. doi: 10.1109/INDUSI S.2010.5565861
  15. 15.
    Qian XM, Wang H, Hou XS (2012) Video text detection and localization in intra-frames of H.264/AVC compressed video. Multimed Tools Appl. doi: 10.1007/s11042-012-1168-z Google Scholar
  16. 16.
    Qin C, Wang SZ, Zhang XP (2012) Simultaneous inpainting for image structure and texture using anisotropic heat transfer model. Multimed Tools Appl 56:469–483. doi: 10.1007/s11042-010-0601-4 CrossRefGoogle Scholar
  17. 17.
    Saipullah KM, Kim DH (2012) A robust texture feature extraction using the localized angular phase. Multimed Tools Appl 59:717–747. doi: 10.1007/s11042-011-0766-5 CrossRefGoogle Scholar
  18. 18.
    Solbo S, Eltoft T (2008) A stationary wavelet-domain wiener filter for correlated speckle. IEEE Trans Geosci Remote 46:1219–1230. doi: 10.1109/TGRS.2007.912718 CrossRefGoogle Scholar
  19. 19.
    Tan JQ, Fang Y (2000) Newton–Thiele’s rational interpo lants. Number Algoritm 24:141–157. doi: 10.1023/A:1019193210259 CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Tegolo D, Isgro F (2001) A genetic algorithm for scratch removal in static images. Proc. ICIAP 2001 pp 507–511. doi:  10.1109/ICIAP.2001.957060
  21. 21.
    Vijaykumar V R, Jothibasu P (2010) Decision based adaptive median filter to remove blotches, scratches, streaks,stripes and impluse noise in images. Proc. ICIP 2010 pp 117–120. doi:  10.1109/ICIP.2010.5651915
  22. 22.
    Xiang YJ, Feng LM, Xie SL, Zhou ZH (2011) An efficient spatio-temporal boundary matching algorithm for video error concealment. Multimed Tools Appl 52:91–103. doi: 10.1007/s11042-009-0457-7 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Computer and Information, School of MathematicsHefei University of TechnologyHefeiPeople’s Republic of China
  2. 2.School of Computer and InformationHefei University of TechnologyHefeiPeople’s Republic of China

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