Open Access

Training Methods for Image Noise Level Estimation on Wavelet Components

EURASIP Journal on Advances in Signal Processing20042004:405209

DOI: 10.1155/S1110865704401218

Received: 25 July 2003

Published: 2 December 2004


The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. The method widely used is based on the mean absolute deviation (MAD). This model-based method assumes specific characteristics of the noise-contaminated image component. Three novel and alternative methods for estimating the noise standard deviation are proposed in this work and compared with the MAD method. Two of these methods rely on a preliminary training stage in order to extract parameters which are then used in the application stage. The sets used for training and testing, 13 and 5 images, respectively, are fully disjoint. The third method assumes specific statistical distributions for image and noise components. Results showed the prevalence of the training-based methods for the images and the range of noise levels considered.

Keywords and phrases

noise estimation training methods wavelet transform image processing

Authors’ Affiliations

Institute of Sound and Vibration Research, University of Southampton
The Foundry


© De Stefano et al. 2004