| Image fusion refers to the techniques that integrate complementary information from multi-image sensor data such that the new images are more suitable for the purpose of human visual perception and computer process such as segmentation, feature extraction, and object recognition. Because different kinds of imaging sensors are optimized for somewhat different operating range and environmental conditions, individual sensor may not receive all the information necessary for detecting an object by human or computer vision. Effective combination of such sensors with different features andlor viewing positions could, therefore, extend the capabilities of the individual ones. Numerous applications that would benefit from the use of multiple sensors include display systems in aviation, remote sensing, surveillance, automated machine vision, and medical imaging.In this thesis, some new methods and applications of pixel-level multisensor image fusion are presented.In chapter 1, the fundamental concept, methods and applications of multisensor image fusion are introduced. Some existed methods for image fusion are averaging and weighted averaging, color mapping, nonlinear method, optimization approach, Markov random fields and simulated annealing, artificial neural networks, pyramids, wavelets. The thesis outline is also given in chapter 1.In chapter 2, three novel filters for the removal of impulse noise from corrupted images are proposed. The first is an improved median filter, which can achieve excellent impulse noise cancellation even when the percentage of impulse noise is high. The second is a fuzzy neuron network based hybrid filter, which comprises four basic components: plus-shaped center weighted median filter, cross-shapedcenter weighted median filter, nine pixels median filter, and a fusion center with fuzzy-neuron network. The proposed filter is able to effectively inherit the merits of the used three filters. The third is a fuzzy reasoning filter based on neural network. The output signal of the filter is obtained as the sum of the input signal and nonlinear transformation of the output of fuzzy reasoning, which is obtained by trained neural network.Chapter 3 is concentrated on multifocus image fusion using artificial neural networks. Two spatially registered images with different focuses are decomposed into several blocks. Then, three features reflecting the clear level of every block, i.e., spatial frequency, visibility, and edge, are calculated. Finally, artificial neural networks, i.e., multilayer-perceptron, radial-basis function, probabilistic neural network, are used to recognize the clear level of the corresponding blocks to decide which blocks should be used to construct the fusion result. Experimental results show that the proposed method can perform better than the wavelet transform based method when the objects in the multifocus images are not intersected. Even when the objects in the original images have some motions or the original images are not stringently registered, the proposed method still perform well. And the proposed method can perform real time.It is well known that discrete wavelet transform yields a shift variant signal representation, which means that a simple integer shift of the input signal will usually result in a nontrivial modification of the decomposed coefficients. Thus an image fusion scheme based on the discrete wavelet transform will also be shift dependent, which is undesirable in practical image fusion process(especially considering misregistration problems). In chapter 4, pyramid-structured discrete wavelet frame transform, discrete wavelet frame packet transform, tree-structureddiscrete wavelet frame transform are used to multisensor image fusion. The concrete fusion process, such as activity level measurement, coefficient combination methods, consistency verification and decomposition depth are discussed in detail.Until recently, multiwavelets, which are extension from scalar wavelets, have received considerab...
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