  Metal artifacts is a major challenge in computed tomography (CT) imaging,
significantly degrading image quality and making accurate diagnosis difficult.
However, previous methods either require prior knowledge of the location of
metal implants, or have modeling deviations with the mechanism of artifact
formation, which limits the ability to obtain high-quality CT images. In this
work, we formulate metal artifacts reduction problem as a combination of
decomposition and completion tasks. And we propose RetinexFlow, which is a
novel end-to-end image domain model based on Retinex theory and conditional
normalizing flow, to solve it. Specifically, we first design a feature
decomposition encoder for decomposing the metal implant component and inherent
component, and extracting the inherent feature. Then, it uses a
feature-to-image flow module to complete the metal artifact-free CT image step
by step through a series of invertible transformations. These designs are
incorporated in our model with a coarse-to-fine strategy, enabling it to
achieve superior performance. The experimental results on on simulation and
clinical datasets show our method achieves better quantitative and qualitative
results, exhibiting better visual performance in artifact removal and image
fidelity
