An adaptive Monte Carlo approach to nonlinear image denoising

This paper introduces a novel stochastic approach to image denoising using an adaptive Monte Carlo scheme. Random samples are generated from the image field using a spatially-adaptive importance sampling approach. Samples are then represented using Gaussian probability distributions and a sample rejection scheme is performed based on a 2 statistical hypothesis test. The remaining samples are then aggregated based on Pearson Type VII statistics to create a non-linear estimate of the denoised image. The proposed method exploits global information redundancy to suppress noise in an image. Experimental results show that the proposed method provides superior noise suppression performance both quantitatively and qualitatively when compared to the state-of-the-art image denoising methods.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Alexander Wong, Akshaya Kumar Mishra, Paul W. Fieguth, David A. Clausi
• Keywords: Computer Vision, ICPR 2008, Image Denoising Methods, Monte Carlo Scheme, Superior Noise Suppression