83 citations · 162 across the 11 of their papers we have counts for
11 papers
Self-Diffusion Driven Blind Imaging
Yanlong Yang, Guanxiong Luo
Optical imaging systems are inherently imperfect due to diffraction limits, lens manufacturing tolerances, assembly misalignment, and other physical constraints. In addition, unavo…
Self-diffusion for Solving Inverse Problems
Guanxiong Luo, Shoujin Huang, Yanlong Yang
We propose self-diffusion, a novel framework for solving inverse problems without relying on pretrained generative models. Traditional diffusion-based approaches require training a…
Unsupervised Radar Point Cloud Enhancement via Arbitrary LiDAR Guided Diffusion Prior
Yanlong Yang, Jianan Liu, Guanxiong Luo +4
In industrial automation, radar is a critical sensor in machine perception. However, the angular resolution of radar is inherently limited by the Rayleigh criterion, which depends…
Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors
Shoujin Huang, Guanxiong Luo, Yunlin Zhao +8
Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to…
Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI
Guanxiong Luo, Shoujin Huang, Martin Uecker
Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality. However, a persistent challenge lies in balancing image quality with imaging speed. This trade-off…
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI
Shoujin Huang, Guanxiong Luo, Xi Wang +6
In general, diffusion model-based MRI reconstruction methods incrementally remove artificially added noise while imposing data consistency to reconstruct the underlying images. How…
Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation
Guanxiong Luo, Xiaoqing Wang, Mortiz Blumenthal +5
Purpose: In this work, we present a workflow to construct generic and robust generative image priors from magnitude-only images. The priors can then be used for regularization in r…
Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction
Guanxiong Luo, Mengmeng Kuang, Peng Cao
The data consistency for the physical forward model is crucial in inverse problems, especially in MR imaging reconstruction. The standard way is to unroll an iterative algorithm in…
Deep, Deep Learning with BART
Moritz Blumenthal, Guanxiong Luo, Martin Schilling +2
Purpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibra…
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
Guanxiong Luo, Moritz Blumenthal, Martin Heide +1
We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. Different from conventional deep learning-based MRI reconstr…
MRI Reconstruction Using Deep Bayesian Estimation
GuanXiong Luo, Na Zhao, Wenhao Jiang +2
Purpose: To develop a deep learning-based Bayesian inference for MRI reconstruction. Methods: We modeled the MRI reconstruction problem with Bayes's theorem, following the recently…