Selecting best mother wavelets for curvelet transform based image de-noising

Improving post-processing quality of medical images has been an active field of research for many years. It has been shown that curvelet transforms are plausible candidates for better image reconstruction. However, selecting best mother wavelets for curvelet transform based image de- noising is one of the challenging tasks. In this study, first generation curvelet transform technique has been revisited for selecting best mother wavelets for image de-noising. Results showed that the bi-orthogonal function biorS.5 performed better for most of the noise suppression cases. By the virtue of linear phase property, bi-orthogonal functions are considered to be most suitable for image reconstruction. Based on the study, it is proposed that the selection of right Mother Wavelet for de- noising improves the quality of post-processed images, consequently making it possible to improve the accuracy of diagnostic imaging.