The theoretical studies indicate digital curvelet transform to be an even better method than wavelets for optical
application. In this paper, a multiscale biometric recognition method based on digital curvelet transform via wrapping is
surveyed and studied. First, all images are decomposed by using curvelet transform. As a result of performing curvelet
transform, curvelet coefficients of low frequency and high frequency in different scales and various angels will be
obtained. Then, low frequency coefficients as study samples to the BP neural network are applied. Finally, low
frequency coefficients of testing image are used to simulate neural network, then recognition results will be obtained.
The experiments are performed on the Cambridge University ORL database, and the results show that the recognition
rate of the curvelet-based method is obviously improved.
To improve the normal medical image fusion algorithm in order to avoid the loss of the detailed information in the
processes of medical image fusion, a multiscale medical image fusion method based on nonsubsampled contourlet
transform(NSCT) is proposed in this paper. First, the source images(MRI and CT images) are decomposed by using
nonsubsampled contourlet transform. Then, the details of contourlet coefficients are fused on each corresponding levels
with a vision feature fusion operator. Finally, the fused image will be obtained by taking the inverse nonsubsampled
contourlet transformation. The experimental results show that the effect of the nonsubsampled contourlet-based method
is obviously improved, and the proposed method can effectively preserve the detailed information of the source images.