Study On Multi-source Image Fusion Based On Multi-scale Transform

Multi-source image fusion means that integrating information of two or more source images to get a new image which can represent the scene exactly, entirely and reliably. Recently, due to such good properties as localization, multi-scale transform has been widely used in image fusion. After the source images are decomposed by using multi-scale transform, the coefficients to be got will belong to different scales then, the corresponding fusion rules will be chosen to fuse the coefficients perfectly to improve the quality of the fused image.In the filed of image fusion algorithm based on multi-scale, the image fusion algorithm based on either Laplacian pyramid or wavelet is mature and used widely. But both of them have some limitations, when the Laplacian pyramid is used to fuse images, some redundant information will be got to make the data size increase in the fusion processing, and the Laplacian pyramid can not represent the directional information of the image accurately. Compared with the Laplacian pyramid, although the wavelet transform can not result in increasing the data size and have some directional information, it can only decompose low frequency signal, not high frequency signal. At the same time, when source images are decomposed by the wavelet, how to get the perfect fusion rule is a problem then, the most important is the wavelet base has no property such as anisotropy so that the wavelet can not represent image sparely. All about above will influence the quality of the fused image. Focusing on these problems, the main contributions of this dissertation are summarized as follows:(1) Aiming at the wavelet can only decompose the low frequency signal, not the high frequency signal, the wavelet packet transform which not only can decompose the low frequency signal but also can decompose high frequency signal is used to decompose and construct the source images then, the infrared and visible images are fused by combining the wavelet packet transform and the improved fusion rules.(2) Aiming at the fusion rule, the pulse coupled neural network (PCNN) is introduced especially and improved effectively then, the improved PCNN is used as fusion rule. At the same time, the wavelet transform based on lifting scheme is proposed to simplify the computations and save the memory spaces then, the medical images are fused by combining the wavelet transform based on lifting scheme and the improved PCNN.(3) Aiming the wavelet base has no property such as anisotropy so that the wavelet can not represent image sparely, the nonsubsampled contourlet transform which has property such as multi-scale and multi-direction is chosen to decompose and construct the source images and then, the multi-focus images are fused by combining the nonsubsampled contourlet transform and morphology.A series of image fusion algorithms used in this dissertation are based on multi-scale transform, when these image fusion algorithms are used to fuse the images, the experimental results show that the good fused images can be got.