  Untrained Neural Network Prior (UNNP) based algorithms have gained increasing
popularity in tomographic imaging, as they offer superior performance compared
to hand-crafted priors and do not require training. UNNP-based methods usually
rely on deep architectures which are known for their excellent feature
extraction ability compared to shallow ones. Contrary to common UNNP-based
approaches, we propose a regularized shallow image prior method that combines
UNNP with hand-crafted prior for Electrical Impedance Tomography (EIT). Our
approach employs a 3-layer Multi-Layer Perceptron (MLP) as the UNNP in
regularizing 2D and 3D EIT inversion. We demonstrate the influence of two
typical hand-crafted regularizations when representing the conductivity
distribution with shallow MLPs. We show considerably improved EIT image quality
compared to conventional regularization algorithms, especially in structure
preservation. The results suggest that combining the shallow image prior and
the hand-crafted regularization can achieve similar performance to the Deep
Image Prior (DIP) but with less architectural dependency and complexity of the
neural network.
