  While medical images such as computed tomography (CT) are stored in DICOM
format in hospital PACS, it is still quite routine in many countries to print a
film as a transferable medium for the purposes of self-storage and secondary
consultation. Also, with the ubiquitousness of mobile phone cameras, it is
quite common to take pictures of the CT films, which unfortunately suffer from
geometric deformation and illumination variation. In this work, we study the
problem of recovering a CT film, which marks the first attempt in the
literature, to the best of our knowledge. We start with building a large-scale
head CT film database CTFilm20K, consisting of approximately 20,000 pictures,
using the widely used computer graphics software Blender. We also record all
accompanying information related to the geometric deformation (such as 3D
coordinate, depth, normal, and UV maps) and illumination variation (such as
albedo map). Then we propose a deep framework to disentangle geometric
deformation and illumination variation using the multiple maps extracted from
the CT films to collaboratively guide the recovery process. Extensive
experiments on simulated and real images demonstrate the superiority of our
approach over the previous approaches. We plan to open source the simulated
images and deep models for promoting the research on CT film recovery
(https://anonymous.4open.science/r/e6b1f6e3-9b36-423f-a225-55b7d0b55523/).
