  Deformable image registration, i.e., the task of aligning multiple images
into one coordinate system by non-linear transformation, serves as an essential
preprocessing step for neuroimaging data. Recent research on deformable image
registration is mainly focused on improving the registration accuracy using
multi-stage alignment methods, where the source image is repeatedly deformed in
stages by a same neural network until it is well-aligned with the target image.
Conventional methods for multi-stage registration can often blur the source
image as the pixel/voxel values are repeatedly interpolated from the image
generated by the previous stage. However, maintaining image quality such as
sharpness during image registration is crucial to medical data analysis. In
this paper, we study the problem of anti-blur deformable image registration and
propose a novel solution, called Anti-Blur Network (ABN), for multi-stage image
registration. Specifically, we use a pair of short-term registration and
long-term memory networks to learn the nonlinear deformations at each stage,
where the short-term registration network learns how to improve the
registration accuracy incrementally and the long-term memory network combines
all the previous deformations to allow an interpolation to perform on the raw
image directly and preserve image sharpness. Extensive experiments on both
natural and medical image datasets demonstrated that ABN can accurately
register images while preserving their sharpness. Our code and data can be
found at https://github.com/anonymous3214/ABN
