Abstract
Many image registration algorithms are formulated as optimization problems with a gradient descent based solver, One difficulty with designing and implementing such methods is in the implementation of the gradient computation. This process can be time-consuming and error-prone. In addition some functions do not have gradients that can be expressed in symbolic form. Automatic differentiation is useful for computing gradients of complicated objective functions. It moves the burden of computing gradients from the programmer to the computer. So far, AD has not been exploited for use in image registration. This paper describes a software library the authors have developed to automate the process of computing gradients of registration objective functions. This can alleviate the job of registration designers somewhat and potentially make it easier to design better registration algorithms.