Coherence enhancement diffusion using Multi-Scale DFB

Diffusion filtering techniques are mostly used to enhance the ridge structure of a noisy fingerprint image. In these filtering techniques the measurement of local orientation is needed. The diffusion tensor used in these techniques reflects the local image structure, as in a structure tensor same set of eigenvectors are used. To control the diffusion along the direction of high coherence special Eigenvalues are chosen. It works well in enhancing the ridges but, it takes orientation angles implicitly by using local image structure (derivatives). As we know that the derivatives have undesirable property of enhancing noise which makes the process of finding the correct orientation more difficult. This gives a further motivation for the improved orientation field calculated by some more reliable mean, which can overcome such difficulties. Therefore, in this work a Multi-Scale DDFB is used which adaptively change the local neighborhood size with the image local contrast and feature width. Experimental results show that the proposed algorithm is noise robust and is more suitable for feature localization as compare to other coherence enhancement diffusion algorithms.