A New Robust Technique for Stabilizing Brightness Fluctuations in Image Sequences
Temporal random variation of luminance in images can manifest in film and video due to a wide variety of sources. Typical in archived films, it also affects scenes recorded simultaneously with different cameras (e.g. for film special effect), and scenes affected by illumination problems. Many applications in Computer Vision and Image Processing that try to match images (e.g. for motion estimation, stereo vision, etc.) have to cope with this problem. The success of current techniques for dealing with this is limited by the non-linearity of severe distortion, the presence of motion and missing data (yielding outliers in the estimation process) and the lack of fast implementations in reconfigurable systems. This paper proposes a new process for stabilizing brightness fluctuations that improves the existing models. The article also introduces a new estimation method able to cope with outliers in the joint distribution of pairs images. The system implementation is based on the novel use of general purpose PC graphics hardware. The overall system presented here is able to deal with much more severe distortion than previously was the case, and in addition can operate at 7 fps on a 1.6GHz PC with broadcast standard definition images.
KeywordsMotion Estimation Graphic Hardware Robust Technique Severe Distortion Global Motion Estimation
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- 1.Decencière, E.: Restauration automatique de films anciens. PhD thesis, Ecole Nationale Supérieure des Mines de Paris (December 1997)Google Scholar
- 2.Gonzalez, R., Wintz, P.: Digital Image Processing, 2nd edn. (1987)Google Scholar
- 4.Jin, H., Favaro, P., Soatto, S.: Real-Time feature tracking and outlier rejection with changes in illumination. In: ICCV, July 2001, pp. 684–689 (2001)Google Scholar
- 5.Kokaram, A.C., Dahyot, R., Pitie, F., Denman, H.: Simultaneous luminance and position stabilization for film and video. In: Visual Communications and Image Processing, San Jose, California USA (January 2003)Google Scholar
- 6.Lai, S.-H., Fang, M.: Robust and efficient image alignement. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Fort Collins, Colorado, June 1999, vol. 2, pp. 167–172 (1999)Google Scholar
- 7.Juang, B.H., Rabiner, L.R.: An introduction to hidden markov models. IEEE ASSP Mag., 4–16 (1986)Google Scholar
- 8.Video Material, URL: http://papabois.mee.tcd.ie/sigmedia/publications/
- 9.Naranjo, V., Albiol, A.: Flicker reduction in old films. In: Proc. of the 2000 International Conference on Image Processing (ICIP 2000) (September 2000)Google Scholar
- 10.Ohuchi, T., Seto, T., Komatsu, T., Saito, T.: A robust method of image flicker correction for heavily-corrupted old film sequences. In: Proc. of the 2000 International Conference on Image Processing (ICIP 2000) (September 2000)Google Scholar
- 12.van Roosmalen, P.M.B.: Restoration of archived film and video. PhD thesis, Delft University of Technology (October 1999)Google Scholar
- 14.Yang, X., Chong, N.: Enhanced approach to film flicker removal. In: Proceedings of SPIE Applications of Digital Image Processing XXIII, vol. 4115, pp. 39–47 (2000)Google Scholar