Reduced complexity Retinex algorithm via the variational approach

Journal of Visual Communication and Image Representation

Volumn 14, Issue 4, 2003, Pages 369-388

  Elad, M ^a   Kimmel, R ^b   Shaked, D ^c   Keshet, R ^c  

^a Stanford University   (United States)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^c HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

Gamma correction; Illumination; Look Up Table; Quadratic programming; Retinex; Volterra filters

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER HARDWARE; COMPUTER SOFTWARE; GRAPH THEORY; ITERATIVE METHODS; QUADRATIC PROGRAMMING; TABLE LOOKUP;

GAMMA CORRECTION; RETINEX; VOLTERRA FILTERS;

IMAGE PROCESSING;

EID: 0347409053     PISSN: 10473203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1047-3203(03)00045-2     Document Type: Article

References (22)

1. Bertsekas, D.P., 1995. Non-Linear Programming. Athena Scientific, Belmont, MA.
2. Blake, A., 1985. Boundary conditions of lightness computation in Mondrian world. Comput. Vision Graphics Image Process. 32, 314-327.
3. Blake, A., Zisserman, A., 1987. Visual Reconstruction. MIT Press, Cambridge, MA.
4. Brainard, D.H., Wandell, B., 1986. Analysis of the Retinex theory of color vision. J. Opt. Soc. Am. A 3, 1651-1661.
5. Durand, F., Dorsey, J., 2002. Fast bilateral filtering for the display of high dynamic range images, ACM Trans. Graphics 21 (3), 257-266.
6. Faugferas, O.D., 1979. Digital image color processing within the framework of a human visual system. IEEE Trans. ASSP 27, 380-393.
7. Geman, S., Geman, D., 1984. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intell. 6, 721-741.
8. Holm, J., 1996. Strategy for Pictorial Digital Image Processing (PDIP). In: Proceedings of the Color Imaging Conference, November, pp. 194-201.
9. Horn, B.K.P., 1974. Determining lightness from an image. Comput. Graphics Image Process. 3, 277-299.
10. Jobson, D.J., Rahman, Z., Woodell, G.A., 1997a. Properties and performance of the center/surround retinex. IEEE Trans. Image Proc. 6, 451-462.
11. Jobson, D.J., Rahman, Z., Woodell, G.A., 1997b. A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Proc. 6.
12. Kimmel, R., Elad, M., Shaked, D., Keshet (Kresch), R., Sobel, I., 2003. A variational framework for Retinex. Int. J. Comput. Vision 52 (1), 7-23.
13. Lagendijk, R.L., Biemond, J., 1991. Iterative Identification and Restoration of Images. Kluwer Academic Publishing, Boston, MA.
14. Land, E.H., 1977. The Retinex theory of color vision. Sci. Am. 237, 108-128.
15. Land, E.H., 1983. Recent advances in the retinex theory and some implications for cortical computations: color vision and the natural image. Proc. Natl. Acad. Sci. USA 80, 5163-5169.
16. Land, E.H., 1986. An alternative technique for the computation of the designator in the Retinex theory of color vision. Proc. Natl. Acad. Sci. USA 83, 3078-3080.
17. Land, E.H., McCann, J.J., 1971. Lightness and the Retinex theory. J. Opt. Soc. Am. 61, 1-11.
18. Luenberger, D.G., 1987. Linear and Non-Linear Programming, second ed. Addison-Wesley, Menlo-Park, CA.
19. Marroquin, J., Mitter, J., Poggio, T., 1987. Probabilistic solution for ill-posed problems in computational vision. J. Am. Statist. Assoc. 82, 76-89.
20. McCann, J.J., Sobel, I., 1998. Experiments with Retinex, HPL Color Summit.
21. Stockham Jr., T.G., 1972. Image processing in the context of a visual model. Proc. IEEE 60, 828-842.
22. Terzopoulos, D., 1986. Image analysis using multigrid relaxation methods. IEEE Trans. PAMI 8, 129-139.