Advertisement

A Sparse Reconstruction Approach to Video Deinterlacing

  • Maria Trocan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 183)

Abstract

With the apparition of digital television and flat displays, interlaced to progressive frame format conversion represents an importantant video systems feature. In this chapter, we use an inverse problem formulation for video deinterlacing and propose a two-step sparse-reconstruction algorithm for solving it. Firstly, an edge-preserving approximation of the progressive frame is obtained and used for triggering a bidirectional motion-compensated prediction for the current field. In a second step, a sparse residual is calculated as difference between the current field and the projection of its temporal prediction using the same parity sampling matrix. This field residual is further reconstructed using a total-variation regularization method and added back to the motion-compensated prediction to form the final progressive frame. The proposed deinterlacing method presents high quality results compared to other deinterlacing approaches.

Keywords

High Quality Result Total Variation Minimization Inverse Optimization Problem Iterative Soft Thresholding Temporal Field Average 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kim, W., Jin, S., Jeong, J.: Novel intra deinterlacing algorithm using content adaptive interpolation. IEEE Trans. Consum. Electron. 53(3), 1036–1043 (2007)CrossRefGoogle Scholar
  2. 2.
    Biswas, M., Kumar, S., Nguyen, T.Q.: Performance analysis of motion-compensated de-interlacing systems. IEEE Transactions on Image Processing 15(9), 2596–2609 (2006)CrossRefGoogle Scholar
  3. 3.
    Mohammadi, H.M., Langlois, P., Savaria, Y.: A five-field motion compensated deinterlacing method based on vertical motion. IEEE Trans. Consum. Electron. 53(3), 1117–1124 (2007)CrossRefGoogle Scholar
  4. 4.
    Fan, Y.C., Lin, H.S., Chiang, A., Tsao, H.W., Kuo, C.C.: Motion compensated deinterlacing with efficient artifact detection for digital television displays. Journal of Display Technology 4(2), 218–228 (2008)CrossRefGoogle Scholar
  5. 5.
    Chen, Y.R., Tai, S.C.: True motion-compensated de-interlacing algorithm. IEEE Transactions on Circuits and Systems for Video Technology 19(10), 1489–1498 (2009)CrossRefGoogle Scholar
  6. 6.
    Trocan, M., Mikovicova, B., Zhanguzin, D.: An adaptive motion-compensated approach for video deinterlacing. Springer’s International Journal on Multimedia Tools and Applications, 1–19 (July 2011), doi:10.1007/s11042-011-0845-7Google Scholar
  7. 7.
    Trocan, M., Mikovicova, B.: Smooth motion-compensated video deinterlacing. In: Proc. of 7th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 143–148 (September 2011)Google Scholar
  8. 8.
    Keller, S., Lauze, F., Nielsen, M.: A Total Variation Motion Adaptive Deinterlacing Scheme. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 408–418. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Keller, S., Lauze, F., Nielsen, M.: An adaptive motion-compensated approach for video deinterlacing. IEEE Transactions on Image Processing 17(11), 2015–2028 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Yin, X., Yuan, J., Lu, X., Zou, M.Y.: De-interlacing technique based on total variation with spatial-temporal smoothness constraint. In: Science in China Series F: Information Sciences, vol. 50, pp. 561–575 (2007), doi:10.1007/s11432-007-0047-0Google Scholar
  11. 11.
    Li, C.: An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing, M.S. thesis, Rice University (September 2009)Google Scholar
  12. 12.
    Wright, S.J., Nowak, R.D., Figueiredo, M.A.T.: Sparse reconstruction by separable approximation. IEEE Transactions on Signal Processing 57(7), 2479–2493 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chan, T.F., Esedoglu, S., Park, F., Yip, A.: Total variation image reconstruction: Overview and recent developments. In: Paragios, N., Chen, Y., Faugeras, O.D. (eds.) Handbook of Mathematical Models in Computer Vision, vol. 2, Springer, New York (2006)Google Scholar
  14. 14.
    Bioucas-Dias, J.M., Figueiredo, M.A.T.: A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Transactions on Image Processing 16(12), 2992–3004 (2007)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences 1(3), 248–272 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Trocan, M., Maugey, T., Tramel, E.W., Fowler, J.E., Pesquet-Popescu, B.: Compressed sensing of multiview images using disparity compensation. In: Proceedings of the International Conference on Image Processing, Hong Kong, pp. 3345–3348 (September 2010)Google Scholar
  17. 17.
    Liu, C.: Beyond Pixels: Exploring New Representations and Applications for Motion Analysis, Ph.D. thesis, Massachusetts Institute of Technology (May 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Signal and Image Processing DepartmentInstitut Supérieur d’Électronique de ParisParisFrance

Personalised recommendations