Image noise detection in global illumination methods based on fast relevance vector machine

Authors:
Joseph Constantin;André Bigand;Ibtissam Constantin;Denis Hamad
Affiliations:
Département des Mathématiques Appliquées, Faculté des Sciences, UL, Jdeidet, Liban,Laboratoire des Physiques Appliquées, PR2N, Faculté des Sciences, UL, Jdeidet, Liba ...;LISIC, ULCO, Calais Cedex, France;Département des Mathématiques Appliquées, Faculté des Sciences, UL, Jdeidet, Liban,Laboratoire des Physiques Appliquées, PR2N, Faculté des Sciences, UL, Jdeidet, Liba ...;LISIC, ULCO, Calais Cedex, France
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

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Abstract

Global illumination methods based on stochastically techniques provide photo-realistic images. However, they are prone to noise that can be reduced by increasing the number of paths as proved by Monte Carlo theory. The problem of finding the number of paths that are required in order to ensure that human observers cannot perceive any noise is still open. In this paper, a novel approach to predict which image highlights perceptual noise is proposed based on Fast Relevance Vector Machine (FRVM). This model can then be used in any progressive stochastic global illumination method in order to find the visual convergence threshold of different parts of any image. A comparative study of this model with experimental psycho-visual scores demonstrates the good consistency between these scores and the model quality measures. The proposed model has been compared also with other learning model like SVM and gives satisfactory performance.