DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration
This work addresses image registration for medical or computer vision applications, but it is incremental as it builds on existing mutual information and neural estimation methods.
The authors tackled the problem of unsupervised image registration by introducing a differentiable end-to-end algorithm that uses a neural estimator for mutual information and matrix exponential for transformation, achieving improved results compared to standard state-of-the-art toolboxes.
In this work, we present a novel unsupervised image registration algorithm. It is differentiable end-to-end and can be used for both multi-modal and mono-modal registration. This is done using mutual information (MI) as a metric. The novelty here is that rather than using traditional ways of approximating MI, we use a neural estimator called MINE and supplement it with matrix exponential for transformation matrix computation. This leads to improved results as compared to the standard algorithms available out-of-the-box in state-of-the-art image registration toolboxes.