Efficiency analysis of information theoretic measures in image registration

Abstract

Efficiency analysis of some information theoretic measures that can be used in image registration as objective functions is carried out. Shannon mutual information, Renyi and Tsallis entropy are examined using synthesized images with correlation function, intensity and noise distributions close to Gaussian. Results show that Renyi entropy potentially provides a faster convergence rate and lower variance of parameters’ estimates when using recurrent image registration algorithms. According to these criteria, Tsallis entropy provides a little worse results; however, it has a larger effective range. Shannon mutual information loses to both entropy measures. Moreover, it is more sensitive to noise. Nevertheless, Shannon mutual information is more effective in terms of computational complexity.