Abstract:
Integration of images from different sources is increasingly used in different visual signal processing applications. But before the integration it is of utmost importance that the images are geometrically aligned and this aligning process is very often referred to as ‘image registration’. There are two approaches of registering the reference and distorted images, viz., feature-based and intensity-based. The intensity-based methods give better accuracy comparing to the feature-based methods by considering entire pixels of the images, instead of considering just few selected geometric features as in the latter method. Traditionally, image registration is carried out in different transform domains to incorporate the facilities that these transforms provide. Discrete wavelet transform (DWT) has widely been used for image registration. But it has poor directional selectivity. The complex wavelet, ridgelet and shearlet transforms proved slight improvement in directional selectivity over the DWT. Recently, the curvelet transform proved its superiority in directional selectivity among all these wavelet-like transforms, being able to properly detect the commonly occurred curve and edge singularities in images. Hence, in this thesis, an image registration algorithm is developed that uses the curvelet coefficients of images.
Commonly-used probabilistic objective functions in the intensity-based registration algorithms include mutual information, joint entropy and cross-correlation of the transform coefficients of the distorted and reference images. None of these functions consider the conditional dependencies among the images which may exist as the images to be registered are usually captured from a same scene. In this thesis, a new conditional entropy-based objective function is developed using a suitable probabilistic modeling of the approximate level curvelet coefficients of images. The suitability of the probability distribution of the curvelet coefficients of images is validated with a standard statistical test of fit. For the purpose of alignment, a linear transformation, viz., affine transform is used. Extensive experimentations are carried out to test the performance of the proposed registration method as compared to other existing methods using commonly-used performance metrics.
