  Deep metrics have been shown effective as similarity measures in multi-modal
image registration; however, the metrics are currently constructed from aligned
image pairs in the training data. In this paper, we propose a strategy for
learning such metrics from roughly aligned training data. Symmetrizing the data
corrects bias in the metric that results from misalignment in the data (at the
expense of increased variance), while random perturbations to the data, i.e.
dithering, ensures that the metric has a single mode, and is amenable to
registration by optimization. Evaluation is performed on the task of
registration on separate unseen test image pairs. The results demonstrate the
feasibility of learning a useful deep metric from substantially misaligned
training data, in some cases the results are significantly better than from
Mutual Information. Data augmentation via dithering is, therefore, an effective
strategy for discharging the need for well-aligned training data; this brings
deep metric registration from the realm of supervised to semi-supervised
machine learning.
