Image Fusion Based On Dual-tree Complex Wavelet Transform

With the development of modem medicine, many medical image devices have been conceived. At the same time, different types of medical images are enhancing their applications in various fields. In the field of medicine, for instances, it is helping to make more accurate and comprehensive diagnosis and treatment. The comprehensive and reciprocal information could be obtained from different medical images within one pathological organization. The medical image fusion technology, as a branch of data fusion, could utilize the redundant information to improve the signal-to-noise ratio so that giving a more reliable result as well as to fuse the reciprocal information in order to obtain a composite image with more detail and complete content. Among fusion methods of different kinds, the Pixel-level Fusion method, which can best keep information from original images, is the prevalent method among professionals and the major study of this dissertation. The major contents of this dissertation are as follow.Firstly, the significance and background of the medical image fusion are proposed in this dissertation. In addition, on the bases of the brief introduction to the commonly used medical image, the development background of image fusion technology, the concept of image fusion and its research value in medical domain are indicated as well. In order to estimate the fusion result, a combination of the subjective and objective evaluation criterion for the performance of image fusion and the effect of the fused images, which is proved reasonable, is put forth.Secondly, with regard to the method in wavelet domain, after introducing wavelet transformation elementary theory and the general wavelet denoising principle, the dual-tree CWT is a valuable enhancement of the traditional real wavelet transform that is nearly shift invariant and, in higher dimensions, directionally selective. Since the real and imaginary parts of the dual-tree CWT are, in fact, conventional real wavelet transforms, the CWT benefits from the vast theoretical, practical, and computational resources that have been developed for the standard DWT.Thirdly, After Intensity-Based Automatic Image Registration, the max-coefficient and min-coefficient fusion algorithms, the wavelet coefficient constant weighting fusion algorithm and the partial coefficient variance weighting fusion algorithm are studied in detail. The dissertation points out the disadvantage of the traditional algorithms that the importance of the fusion algorithms.In this paper, we combine apply to image fusion, using the properties to decompose and reconstruct and compare with the traditional wavelets. The Dual-Tree discrete wavelet is a new wavelet transform. It not only keeps the multi-resolution and the analytic ability to time-frequency localizability, but also provides good directional selectivity, shift invariance and limited data redundancy that is lack in the traditional wavelet transform. It is just the shift invariance of DT-CWT solved the problem, variation of energy in each level of coefficient for the shift of signal, which cannot be solved using real wavelet transform; The good directional selectivity makes DT-CWT get eight sub bands in every level, the high-frequency which reflect detail feature can describe six directional characteristics respectively, so it can obtain more information about gray graph in different level.