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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kajiya, J.: The Rendering Equation. ACM Computer Graphics 20(4), 143–150 (1986)
Shirley, P., Wang, C., Zimmerman, K.: Monte Carlo Techniques for Direct Lighting Calcultations. ACM Transactions on Graphics 15(1), 1–36 (1996)
Lahoudou, A., Viennet, E., Beghdadi, A.: Section Low-level Features for Image Quality Assessment by Stastical Methods. Journal of Computing and Information Technology 2, 183–189 (2010)
Gilboa, G., Sochen, N., Zeevi, Y.Y.: Estimation of Optimal PDE-Based Denoising in the SNR Sense. IEEE Transactions on Image Processing 15(8), 2269–2280 (2006)
Horé, A., Ziou, D.: Image Quality Metrics, PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition, pp. 2366–2369 (2010)
Ferzli, R., Karam, L.: No-reference Objective Wavelet Based Immune Image Sharpness Metric. In: International Conference on Image Processing (2005)
Zhang, J., Ong, S., Thinh, M.: Kurtosis Based No-reference Quality Assessment of JPEG2000 Images. Signal Processing: Image Communication 26, 13–23 (2011)
Wang, Z., Simoncelli, E.P.: Reduced Reference Image Quality Assessment Using a Wavelet-domain Natural Image Statistic Model. In: Proc. SPIE, Conf. on Human Vision and Electronic Imaging X, vol. 5666, pp. 149–159 (January 2005)
Li, Q., Wang, Z.: Reduced-Reference Image Quality Assessment using Divisive Normalization Based Image Representation. IEEE Journal of Selected Topics in Signal Processing 3(2), 202–211 (2009)
Farrugia, J.P., Péroche, B.: A Progressive Redering Algorithm using an Adaptive Perceptually Based Image Metric. Comput. Graph. Forum 23(3), 605–614 (2004)
Longhurst, P., Debattista, K., Chalmers, A.: A GPU Based Saliency Map for High-fidelity Selective Rendering. In: AFRIGRAPH 2006 4th International Conference on Computer Graphics, Virtual Reality, Visulisation and Interaction in Africa, ACM SIGGRAPH, pp. 21–29 (2006)
Tipping, M.E.: Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research 1, 211–244 (2001)
Tipping, M.E.: Fast Marginal Likelihood Maximisation for Sparse Bayesian Models. In: Bishop, C.M., Frey, B.J. (eds.) Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, January 3-6 (2003)
Kim, J., Suga, Y., Won, S.: A New Approach to Fuzzy Modeling of Nonlinear Dynamic Systems With Noise: Rlevance Vector Learning Machine. IEEE Tranactions on Fuzzy Systems 4(2) (April 2006)
Chen, Z., Tang, H.: Sparse Bayesian Approach to Classification. In: IEEE Proceedings on Networking, Sensing and Control, pp. 914–917 (2005)
Faul, A.C., Tipping, M.E.: Analysis of Sparse Bayesian Learning. In: Advances in Neural Information Processing Systems, vol. 14, pp. 383–389. MIT Press (2002)
Carnet, M., Callet, P.L., Barba, D.: Objective Quality Assessment of Color Images Based on a Generic Perceptual Reduced Reference. Image Communication 23(4), 239–256 (2008)
Chang, S.G., Yu, B., Vetterli, M.: Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising. IEEE Trans. on Image Processing 9(9), 1522–1531 (2000)
Tipping, M.E.: Bayesian Inference: An Introduction to Principles and Practice in Machine Learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) Machine Learning 2003. LNCS (LNAI), vol. 3176, pp. 41–62. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Constantin, J., Bigand, A., Constantin, I., Hamad, D. (2013). Image Noise Detection in Global Illumination Methods Based on Fast Relevance Vector Machine. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-38682-4_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38681-7
Online ISBN: 978-3-642-38682-4
eBook Packages: Computer ScienceComputer Science (R0)