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Found 5 papers, 2 papers with code
Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler
Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields.
A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-based Diffusion Models for Electrical Impedance Tomography
This study aims to investigate the potential of three DGMs-variational autoencoder networks, normalizing flow, and score-based diffusion model-to learn implicit regularizers in learning-based EIT imaging.
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements.
Enhancing Electrical Impedance Tomography reconstruction using Learned Half-Quadratic Splitting Networks with Anderson Acceleration
Our Anderson acceleration scheme to enhance HQSNet is generic and can be applied to improve the performance of various physics-embedded deep learning methods.
MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction
To motivate our work, we review several existing priors, namely the truncated Gaussian prior, the $l_1$ prior, the total variation prior, and the deep image prior (DIP).
