MAD-based Estimation of the Noise Level in the Contourlet Domain

Abstract

Noise-level estimation remains one of the most critical issues related to the contourlet-based approaches. In this paper, an investigation of an effective solution is directed from any redundant contourlet expansion. This is going to be addressed for the first time in that domain. In this proposition, the noise level is estimated as the median absolute value (MAD) of the finest multi-scale coefficients, calibrated by three correction parameters. This is done according to some visual classification of the natural images. The present estimator provides a better compromise between the image and the contourlet expansion nature, which makes the estimation results more accurate for a wide range of natural images, when compared to the best state-of-the-art methods. Therefore, it is extensively recommended for most of the contourlet-based image applications (Thresholding, filtering, etc.) thanks to its accuracy, simplicity, and rapidity.