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.
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 • 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).