Fingerprint Image Denoising Using Curvelet Transform

Curvelet transform is the new member of the evolving family of multiscale geometric transforms. It offers an effective solution to the problems associated with image denoising using wavelets. Finger prints possess the unique properties of distinctiveness and persistence. However, their image contrast is poor due to mixing of complex type of noise. In this paper an attempt has been made to present the results of denoising of such images using both wavelet and curvelet transforms. The results obtained demonstrate that the curvelet transform based reconstructions are visually sharper than the wavelet reconstructions. The recovery of edges and of the faint linear and curvilinear features is of particularly superior quality. The results obtained are in accordance with the expected predictions of the existing theory of curvelet transforms.