  Image registration is a fundamental task for medical imaging. Resampling of
the intensity values is required during registration and better spatial
resolution with finer and sharper structures can improve the resampling
performance and hence the registration accuracy. Super-resolution (SR) is an
algorithmic technique targeting at spatial resolution enhancement which can
achieve an image resolution beyond the hardware limitation. In this work, we
consider SR as a preprocessing technique and present a CNN-based resolution
enhancement module (REM) which can be easily plugged into the registration
network in a cascaded manner. Different residual schemes and network
configurations of REM are investigated to obtain an effective architecture
design of REM. In fact, REM is not confined to image registration, it can also
be straightforwardly integrated into other vision tasks for enhanced
resolution. The proposed REM is thoroughly evaluated for deformable
registration on medical images quantitatively and qualitatively at different
upscaling factors. Experiments on LPBA40 brain MRI dataset demonstrate that REM
not only improves the registration accuracy, especially when the input images
suffer from degraded spatial resolution, but also generates resolution enhanced
images which can be exploited for successive diagnosis.
