Abstract
This paper introduces a new method of converting interlaced video to a progressively scanned video and image. The new method is derived from Bayesian framework with the spatial-temporal smoothness constraint and the MAP is done by minimizing the energy functional. The half-quadratic regularization method is used to solve the corresponding partial differential equations (PDEs). This approach gives the improved results over the conventional de-interlacing methods. Two criteria are proposed in the paper, and they can be used to evaluate the performance of the de-interlacing algorithms.
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References
Tschumperlé D, Besserer B. High quality deinterlacing using inpainting and shutter-model directed temporal interpolation. In: ICCVG’2004. http://www.prestospace.org/training/images/iccvg2004.pdf
Schultz E, Stevenson R. Motion-compensated scan conversion of interlaced video sequences. In: The Proceedings of SPIE, Image and Video Processing IV. San Jose, CA, USA, 1996, 2666: 107–118
Choi K, Ko S J. POCS-based enhancement of de-interlaced video in the DCT domain. In: Proc. of 2003 International Technical Conference on Circuits Systems, Computers and Communications. Phoenix Park, 2003, 2: 1290–1293
Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D, 1992, 60: 259–268
Chambolle A, Lions L P. Image recovery via total variational minimization and related problems. Numer Math, 1997, 76: 167–188
Keller S, Lauze F, Nielsen M. A total variation motion adaptive deinterlacing scheme. In: Ron K, Nir S, Joachim W, eds. Scale Space and PDE Methods in Computer Vision: 5th International Conference, Scale-Space 2005. Berlin: Springer-Verlag, GmbH, 2005. 408–418
Zou M Y. Deconvolution and Signal Recovery (in Chinese). Beijing: National Defence Industry Press, 2001
Charbonnier P, Feraud L B, Aubert G, et al. Deterministic edge-preserving regularization in computed imaging. IEEE Trans Image Proc, 1997, 6(2): 298–311
Teboul S, Laure B F, Aubert G, et al. Variational approach for edge-perserving regularization using coupled PDE’s. IEEE Trans Image Proc, 1998, 7(3): 387–397
Geman D, Yang C. Nonlinear image recovery with half-quadratic regularization. IEEE Trans Image Proc, 1995, 4(7): 932–946
Yin X M, Yuan J H, Zuo M Y. New de-interlacing algorithm based on edge-dependent interpolation and motion compensation. J Data Acquis Proc, 2007, 22(1): 56–66
Bovik A. Handbook of Image and Video Processing. New York: Academic Press, 2000
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Yin, X., Yuan, J., Lu, X. et al. De-interlacing technique based on total variation with spatial-temporal smoothness constraint. SCI CHINA SER F 50, 561–575 (2007). https://doi.org/10.1007/s11432-007-0047-0
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DOI: https://doi.org/10.1007/s11432-007-0047-0