  Fluoroscopy is an imaging technique that uses X-ray to obtain a real-time 2D
video of the interior of a 3D object, helping surgeons to observe pathological
structures and tissue functions especially during intervention. However, it
suffers from heavy noise that mainly arises from the clinical use of a low dose
X-ray, thereby necessitating the technology of fluoroscopy denoising. Such
denoising is challenged by the relative motion between the object being imaged
and the X-ray imaging system. We tackle this challenge by proposing a
self-supervised, three-stage framework that exploits the domain knowledge of
fluoroscopy imaging. (i) Stabilize: we first construct a dynamic panorama based
on optical flow calculation to stabilize the non-stationary background induced
by the motion of the X-ray detector. (ii) Decompose: we then propose a novel
mask-based Robust Principle Component Analysis (RPCA) decomposition method to
separate a video with detector motion into a low-rank background and a sparse
foreground. Such a decomposition accommodates the reading habit of experts.
(iii) Denoise: we finally denoise the background and foreground separately by a
self-supervised learning strategy and fuse the denoised parts into the final
output via a bilateral, spatiotemporal filter. To assess the effectiveness of
our work, we curate a dedicated fluoroscopy dataset of 27 videos (1,568 frames)
and corresponding ground truth. Our experiments demonstrate that it achieves
significant improvements in terms of denoising and enhancement effects when
compared with standard approaches. Finally, expert rating confirms this
efficacy.
