Multiresolution image registration based on wavelet transform

Image registration is the process of estimating an optimal geometrical transformation between images. The main goal of image registration methods is to find transformation parameters between references and rotated and/or translated and/or scaled input images, assuming that affine geometrical transformation exists. In this thesis, a multi-resolution approach of wavelet transform (WT) has been used to perform image registration operation. Wavelet transform based image registration methods are iterative so optimization methods are needed to decrease computational time and increase robustness. Of the optimization methods, genetic algorithm (GA) and simultaneous perturbation stochastic approximation (SPSA) have been combined with multi-resolution scheme to overcome drawbacks of method. Normalized cross correlation and normalized mutual information have been used to measure similarity of images.Image registration implementations and performance tests have been performed in Matlab. In this thesis, five different application of wavelet transform based image registration have been implemented. These are full search algorithm with using normalized cross-correlation, simultaneous perturbation stochastic approximation based registration with using normalized cross-correlation and normalized mutual information, and genetic algorithm based image registration with using normalized cross-correlation and mutual information. Image registration methods have been tested with both noiseless images and noisy images. SPSA based method with using mutual information achieves high accuracy and gives the best results in terms of computational time.