Image denoising using spatial context modeling of wavelet coefficients

Abstract

The choice of threshold in wavelet based image denoising is very critical. The universal threshold is a global threshold utilized for denoising the wavelet coefficients. An effective approach for the estimation of universal threshold based on spatial context modeling of the wavelet coefficients has been proposed. Spatial context modeling involves determination of the correlated pixels within a local neighborhood of the pixel to be denoised. Thus the threshold determination depends on the pixel characteristics and not on the size of the image to be denoised. The spatial context information of the wavelet coefficients are computed using the range filter employed in the formation of bilateral filter. Experiments on several Gaussian noise corrupted images show that the proposed method outperforms other thresholding methods such as VisuShrink, SureShrink and BayesShrink.