A Novel Monte Carlo Noise Reduction Operator

Ruifeng Xu, Sumanta N. Pattanaik
School of Computer Science, UCF, Orlando, Fl-32816
Computer Graphics, March/April 2005 (vol. 25 no. 2), p.31-35


   title={A novel Monte Carlo noise reduction operator},

   author={Xu, R. and Pattanaik, S.N.},

   journal={IEEE Computer Graphics and Applications},




   publisher={Published by the IEEE Computer Society}


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 512X512 image.
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