A Post-Processing Deconvolution Step for Wavelet-Based Image Denoising Methods

In this letter, we show that the performance of image denoising algorithms using wavelet transforms can be improved by a post-processing deconvolution step that takes into account the inherent blur function created by the considered wavelet based denoising system. The interest of the proposed deblurring procedure is illustrated on denoised images reconstructed by shrinkage of curvelet and undecimated wavelet coefficients. Experimental results reported here show that the proposed post-processing technique yields improvements in term of image quality and lower mean square error, especially when the image is corrupted by strong additive white Gaussian noise.