Paper ID: 2503.10015 • Published Mar 13, 2025
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging
TL;DR
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Dynamic imaging involves the reconstruction of a spatio-temporal object at
all times using its undersampled measurements. In particular, in dynamic
computed tomography (dCT), only a single projection at one view angle is
available at a time, making the inverse problem very challenging. Moreover,
ground-truth dynamic data is usually either unavailable or too scarce to be
used for supervised learning techniques. To tackle this problem, we propose
RSR-NF, which uses a neural field (NF) to represent the dynamic object and,
using the Regularization-by-Denoising (RED) framework, incorporates an
additional static deep spatial prior into a variational formulation via a
learned restoration operator. We use an ADMM-based algorithm with variable
splitting to efficiently optimize the variational objective. We compare RSR-NF
to three alternatives: NF with only temporal regularization; a recent method
combining a partially-separable low-rank representation with RED using a
denoiser pretrained on static data; and a deep-image prior-based model. The
first comparison demonstrates the reconstruction improvements achieved by
combining the NF representation with static restoration priors, whereas the
other two demonstrate the improvement over state-of-the art techniques for dCT.
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