Performance study of despeckling algorithm for different wavelet transforms

This paper proposes an efficient approach for removing speckle noise from images by using threshold estimation methods, based on Double Density Wavelet Transform (DDWT). The performance of the proposed de-speckling algorithm is well enhanced by applying two different threshold estimation techniques at different decomposition levels of DDWT. The choice of estimation of the threshold value is carried out by VisuShrink and Normal Shrink rules. In this technique, the image is decomposed into nine sub bands using DDWT. The threshold values are estimated from each of the detail sub band in such a way that, using VisuShrink for first level DDWT decomposition and Normal Shrink for higher levels. After that, soft thresholding technique is used for noise suppression. Subsequently the inverse transform is applied to obtain the denoised image. Also the performance of this approach is investigated on Dual tree real, Dual tree complex, Double Density Dual tree real and Double Density dual tree complex wavelet transforms till fifth level decomposition. Computer simulations are carried out to investigate the performance of the proposed method and the results are compared with the existing methods. The results show that the proposed method achieves excellent performance.