Fuzzy thresholding in wavelet domain for speckle reduction in Synthetic Aperture Radar images

TL;DR

This paper introduces a fuzzy thresholding method in the wavelet domain for SAR image despeckling, improving noise reduction by adaptively determining thresholds on wavelet coefficients after logarithmic transformation.

Contribution

It proposes a novel fuzzy thresholding approach for wavelet-based speckle noise reduction in SAR images, enhancing automatic threshold selection and despeckling performance.

Findings

Effective noise reduction demonstrated on test images

Outperforms most existing despeckling methods

Single-level wavelet decomposition used for efficiency

Abstract

The application of wavelet transforms to Synthetic Aperture Radar (SAR) imagery has improved despeckling performance. To deduce the problem of filtering the multiplicative noise to the case of an additive noise, the wavelet decomposition is performed on the logarithm of the image gray levels. The detail coefficients produced by the bidimensional discrete wavelet transform (DWT-2D) needs to be thresholded to extract out the speckle in highest subbands. An initial threshold value is estimated according to the noise variance. In this paper, an additional fuzzy thresholding approach for automatic determination of the rate threshold level around the traditional wavelet noise thresholding (initial threshold) is applied, and used for the soft or hard-threshold performed on all the high frequency subimages. The filtered logarithmic image is then obtained by reconstruction from the thresholded coefficients. This process is applied a single time, and exclusively to the first level of decomposition. The exponential function of this reconstructed image gives the final filtered image. Experimental results on test images have demonstrated the effectiveness of this method compared to the most of methods in use at the moment.