“Poisson matting” by Sun, Jia, Tang and Shum

  • ©Jian Sun, Jiaya Jia, Chi-Keung Tang, and Heung-Yeung Shum




    Poisson matting



    In this paper, we formulate the problem of natural image matting as one of solving Poisson equations with the matte gradient field. Our approach, which we call Poisson matting, has the following advantages. First, the matte is directly reconstructed from a continuous matte gradient field by solving Poisson equations using boundary information from a user-supplied trimap. Second, by interactively manipulating the matte gradient field using a number of filtering tools, the user can further improve Poisson matting results locally until he or she is satisfied. The modified local result is seamlessly integrated into the final result. Experiments on many complex natural images demonstrate that Poisson matting can generate good matting results that are not possible using existing matting techniques.


    1. BERMAN, A., VLAHOS, P., AND DADOURIAN, A. 2000. Comprehensive method for removing from an image the background surrounding a selected object. U.S. Patent 6,134,345.Google Scholar
    2. CHUANG, Y.-Y., CURLESS, B., SALESIN, D. H., AND SZELISKI, R. 2001. A bayesian approach to digital matting. In Proceedings of CVPR 2001, Vol. II, 264–271.Google Scholar
    3. CHUANG, Y.-Y., AGARWALA, A., CURLESS, B., SALESIN, D. H., AND SZELISKI, R. 2002. Video matting of complex scenes. In Proceedings of ACM SIGGRAPH 2002, 243–248. Google ScholarDigital Library
    4. ELDER, J. H., AND GOLDBERG, R. M. 2001. Image editing in the contour domain. IEEE Trans. Pattern Anal. Machine Intell. 23(3): 291–296. Google ScholarDigital Library
    5. FATTAL, R., LISCHINSKI, D., AND WERMAN, M. 2002. Gradient domain high dynamic range compression. In Proceedings of ACM SIGGRAPH 2002, 249–256. Google ScholarDigital Library
    6. FINLAYSON, G. D., HORDLEY, S. D., AND DREW, M. S. 2002. Removing shadows from images. In Proceedings of ECCV 2002, Vol. IV, 823–836. Google ScholarDigital Library
    7. HILLMAN, P., HANNAH, J., AND RENSHAW, D. 2001. Alpha channel estimation in high resolution images and image sequences. In Proceedings of CVPR 2001, Vol. I, 1063–1068.Google ScholarCross Ref
    8. MITSUNAGA, T., YOKOYAMA, T., AND TOTSUKA, T. 1995. Autokey: Human assisted key extraction. In Proceedings of ACM SIGGRAPH 1995, 265–272. Google ScholarDigital Library
    9. NARASIMHAN, S., AND NAYAR, S. 2003. Interactive deweathering of an image using physical models. IEEE Workshop on Color and Photometric Methods in Computer Vision 2003.Google Scholar
    10. P. GILL, W. M., AND WRIGHT, M. 1981. Practical optimization. Academic Press, Boston, MA, USA.Google Scholar
    11. PÉREZ, P., GANGNET, M., AND BLAKE, A. 2003. Poisson image editing. In Proceedings of ACM SIGGRAPH 2003, 313–318. Google ScholarDigital Library
    12. PERONA, P., AND MALIK., J. 1990. Scale space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Machine Intell. 12(7):629-639. Google ScholarDigital Library
    13. QIAN, R. J., AND SEZAN, M. I. 1999. Video background replacement without a blue screen. In Proceedings of ICIP 1999, 143–146.Google ScholarCross Ref
    14. RUZON, M. A., AND TOMASI, C. 2000. Alpha estimation in natural images. In Proceedings of CVPR 2000, 18–25.Google ScholarCross Ref
    15. SMITH, A. R., AND BLINN, J. F. 1996. Blue screen matting. In Proceedings of ACM SIGGRAPH 1996, 259–268. Google ScholarDigital Library

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