Wavelet thresholding of multivalued images

In this paper, a denoising technique for multivalued images exploiting interband correlations is proposed. A redundant wavelet transform is applied and denoising is applied by thresholding wavelet coefficients. Specific functions of the wavelet coefficients are defined that exploit interscale and/or interband correlation of the signal. Three functions are studied: the square of the wavelet coefficients, products of coefficients at adjacent scales, and products of coefficients from different bands. For these functions, the signal and noise probability density functions (pdf) become more separated. The high signal correlation between bands is exploited by summing these products over all bands, in this way separating noise and signal pdfs even more. The noise pdf of the proposed quantities is derived analytically and from this, a wavelet threshold is derived. The technique is demonstrated to outperform single band wavelet thresholding on multispectral remote sensing images and on multimodal MRI images.

I. Introduction

With THE evolution of imaging technology, an increasing number of imaging modalities becomes available. In remote sensing, sensors are available that can generate multispectral or hyperspectral data, involving high numbers of bands. In medical imagery, distinct image modalities reveal different features of the internal body. Examples are MRI images acquired by using different imaging parameters (T1, T2, proton density, diffusion,…), and different CT and nuclear medicine imaging modalities.