Reconfigurable Implementation of Wavelet based Image Denoising

A reconfigurable system for denoising images based on the statistical modeling of wavelet coefficients is presented. The basic algorithm calls for approximating the noisy wavelet coefficients based on observing their local neighbourhood. The architecture for the lifting based wavelet transform followed by the windowing technique for neighbourhood observation has been implemented keeping real time applications in the picture. The architecture has regular data flow and is adaptable to arbitrary image sizes. The wavelet used is the Daubechies´ 9/7 biorthogonal wavelet. The PSNR results for different test images have been presented along with hardware and timing results.