  Cross-modal image synthesis is a topical problem in medical image computing.
Existing methods for image synthesis are either tailored to a specific
application, require large scale training sets, or are based on partitioning
images into overlapping patches. In this paper, we propose a novel Dual
cOnvolutional filTer lEarning (DOTE) approach to overcome the drawbacks of
these approaches. We construct a closed loop joint filter learning strategy
that generates informative feedback for model self-optimization. Our method can
leverage data more efficiently thus reducing the size of the required training
set. We extensively evaluate DOTE in two challenging tasks: image
super-resolution and cross-modality synthesis. The experimental results
demonstrate superior performance of our method over other state-of-the-art
methods.
