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Issue No.02 - March/April (2005 vol.25)
pp: 31-35
Ruifeng Xu , University of Central Florida
Sumanta N. Pattanaik , University of Central Florida
ABSTRACT
We propose a novel Monte Carlo noise reduction operator in this article. We apply and extend the standard bilateral filtering method and build a new local adaptive noise reduction kernel. It first computes an initial estimate for the value of each pixel, and then applies bilateral filtering using this initial estimate in its range filter kernel. It is simple both in formulation and implementation. The new operator is robust and fast in the sense that it can suppress the outliers, as well as the interpixel incoherence in a noniterative way. It can be easily integrated into existing rendering systems as a postprocessing step. The results of our approach are compared with those of other methods. A GPU implementation of our algorithm runs in 500ms for a 512<font face="Arial">X</font>512 image.
INDEX TERMS
Monte Carlo method, noise reduction, bilateral filtering, global illumination, image processing
CITATION
Ruifeng Xu, Sumanta N. Pattanaik, "A Novel Monte Carlo Noise Reduction Operator", IEEE Computer Graphics and Applications, vol.25, no. 2, pp. 31-35, March/April 2005, doi:10.1109/MCG.2005.31
REFERENCES
1. J. Kajiya, "The Rendering Equation," Proc. ACM Siggraph, ACM Press, 1986, pp. 143-150.
2. M.E. Lee and R.A. Redner, "Filtering: A Note on the Use of Nonlinear Filtering in Computer Graphics," Computer Graphics, vol. 10, no. 3, 1990, pp. 23-29.
3. H.E. Rushmeier and G.J. Ward, "Energy Preserving Non-Linear Filters," Proc. ACM Siggraph, ACM Press, 1994, pp. 131-138.
4. H.W. Jensen and N.J. Christensen, "Optimizing Path Tracing using Noise Reduction Filters," J. Winter School of Computer Graphics (WSCG), vol. 3, no. 1, 1995, pp. 134-142.
5. R. Tamstorf and H.W. Jensen, "Adaptive Sampling and Bias Estimation in Path Tracing," Rendering Techniques 97, J. Dorsey and P.H. Slusallek, eds., Springer-Verlag, 1997, pp. 285-295.
6. M.D. McCool, "Anisotropic Diffusion for Monte Carlo Noise Reduction," ACM Trans. Graphics, vol. 18, no. 2, 1999, pp. 171-194.
7. C. Tomasi and R. Manduchi, "Bilateral Filtering for Gray and Color Images," Proc. IEEE 6th Int'l Conf. Computer Vision (ICCV), IEEE CS Press, 1998, pp. 839-846.
8. M. Elad, "Analysis of the Bilateral Filter," Proc. 36th Asilomar on Signals, Systems and Computers, IEEE Press, 2002, pp. 483-487.
9. M. Elad, "On the Origin of the Bilateral Filter and Ways to Improve It," IEEE Trans. Image Processing, vol. 11, no. 10, 2002, pp. 1141-1151.
10. T.R. Jones, F. Durand, and M. Desbrun, "Non-Iterative, Feature-Preserving Mesh Smoothing," Proc. ACM Siggraph, ACM Press, 2003, pp. 943-949.
11. S. Fleishman, I. Drori, and D. Cohen-Or, "Bilateral Mesh Filtering," Proc. ACM Siggraph, ACM Press, 2003, pp. 950-953.
12. F. Durand and J. Dorsey, "Fast Bilateral Filtering for the Display of High-Dynamic-Range Images," Proc. ACM Siggraph, ACM Press, 2002, pp. 257-266.
13. D. Barash, "Bilateral Filtering and Anisotropic Diffusion: Towards a Unified Viewpoint," Proc. 3rd Int'l Conf. ScaleSpace and Morphology, Springer-Verlag, 2001, pp. 273-280.
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