no code implementations • 24 Oct 2023 • Huihui Wang, Guixian Xu, Qingping Zhou
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.
1 code implementation • 24 Oct 2023 • Jiayu Qian, Yuanyuan Liu, Jingya Yang, Qingping Zhou
Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields.
1 code implementation • 30 Jul 2023 • Qingping Zhou, Jiayu Qian, Junqi Tang, Jinglai Li
We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements.
no code implementations • 16 Apr 2023 • Guixian Xu, Huihui Wang, Qingping Zhou
Our Anderson acceleration scheme to enhance HQSNet is generic and can be applied to improve the performance of various physics-embedded deep learning methods.
no code implementations • 8 Apr 2023 • Chen Cheng, Qingping Zhou
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).