Study On Image Fusion Based On Nonsubsampled Contourlet Transform

Multi-sensor image fusion will integrate multiple images derived from the same scene or target collected into a new image to obtain more accurate and more complete description about the scene or target. Image fusion process can take advantage of the complementary information and redundant information in different source images, so as to obtain a fusion result with higher reliability, less blur and better intelligibility. As a result, the fusion image is more suitable for human vision perception and computer processing, such as detection, classification and identification.The basic concepts and theories of the image fusion technology are analyzed and summarized in this thesis. On the basis of the current image fusion algorithm, this thesis has analyzed and researched some typical image fusion algorithms to look for the new ways to retain more useful information of source images and improve fusion image quality effectively. The main contents are as follows:1. From the level structure of image fusion, the basic process and basic methods of multi-source image fusion are discussed, while the evaluation criteria and their selection principles of image fusion effect are summarized Then the transform domain image fusion technology based on multi-resolution analysis is analysized, and the fusion effect of several typical fusion methods in transform domain are compared by experiments.2. On the basis of researching multi-source image fusion and Non-subsampled Contourlet transform theory, a multi-focus image fusion method is proposed based on regional characteristics. In order to effectively obtain the edge and detail information, the source images are decomposed under multi-scale by use of Non-subsampled Contourlet transform, while the corresponding fusion rules are employed to according to the regional characteristic sand approach degree of subband coefficients. The fusion effects of this method are better than those of traditional space domain fusion methods and transform domain fusion methods on pixel level.3. In the design of fusion rule on questions of Non-subsampled Contourlet transform decomposition obtained coefficient of low-frequency subband and high frequency subband coefficients are different design options:selection at low-frequency sub-band coefficients, a high coefficient based on the conformance testing; at select high-frequency sub-band coefficient, the introduction of Pulse-Coupled Neural Network(PCNN) proposed an adaptive coefficient PCNN options.Large numbers of experimental data show that the fusion rule proposed in this paper there is a strong advantage.