Research Of Multi-source Image Fusion Methods In Transform Domain

Multi-source 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.Multi-source image fusion can be divided into space domain image fusion and transform domain image fusion. The transform domain fusion methods utilize a multi-scale, multi-resolution analysis on the expression of the advantages on local signal characteristics, so that it makes up the lack of expression in detail for space domain fusion methods. However, such fusion methods change transform coefficients, which is likely to cause loss of source image information in inappropriate fusion rules.In this thesis, most of the research works focus on multi-source image fusion in transform domain. From the benefit point of scene understand and target identification, this research is to analyze the transform domain image fusion of basic theory and advanced algorithms, and 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 Mmulti-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 nonsubsampled Contourlet transform, while the corresponding fusion rules are employed to according to the regional characteristics and 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) Combined with pulse coupled neural network, the multi-source image fusion methods in transform domain are researched. In multi-focus image fusion, the non-subsampled Contourlet transform is used to the capture feature information of source images, and according to the neighborhood approach degree in ignition mapping images of pulse coupled neural network, the fusion rules are provided to sub-band coefficients from non-subsampled Contourlet transform. According to the requirements of infrared and visible light image fusion, the self-constraint restrictive function is used to define neuron link strength, which is introduced into iteration of pulse coupled neural network. On this basis, fusion process is performed on the subband coefficients generated under non-subsampled Contourlet transform. The proposed corresponding fusion methods for multi-focus image and the infrared and visible images are able to effectively improve the quality of fused images.(4) The multi-objective optimization problem of multi-source image fusion is researched in transform domain. Based on the analysis of multi-objective optimization theory and algorithms, an adaptive differential evolution algorithm is proposed. With adaptive variance factor, dynamical crossover probability function and optimal elite ordering strategy, the algorithm reflects not only good search capability but also good convergence. When applied to multi-objective optimization of multi-source image fusion of transform domain, it will be an effective solution to the comprehensive evaluation in the image fusion process.