7 citations · 23 across the 19 of their papers we have counts for
19 papers
Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement
Yifan Chen, Jiaming Liu, Ye Zheng +2
Underwater image enhancement (UIE) aims to recover clear images from observations affected by wavelength-dependent absorption, scattering, and spatially nonuniform degradation. Alt…
TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy
Zihao Zou, Shirin Shoushtari, Jiaming Liu +6
Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins. The signa…
Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time Training
Edward P. Chandler, Shirin Shoushtari, Jiaming Liu +2
Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior mo…
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
Zihao Zou, Jiaming Liu, Shirin Shoushtari +3
Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input. While diffusion probab…
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models
Marien Renaud, Jiaming Liu, Valentin de Bortoli +2
Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging inverse problems. The recent plug-and-play unadjusted Langevin algorithm (PnP-ULA) has emer…
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Shirin Shoushtari, Jiaming Liu, Edward P. Chandler +2
Plug-and-Play (PnP) priors is a widely-used family of methods for solving imaging inverse problems by integrating physical measurement models with image priors specified using imag…
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images
Mingjie Pan, Yulu Gan, Fangxu Zhou +4
Three-dimensional microscopy is often limited by anisotropic spatial resolution, resulting in lower axial resolution than lateral resolution. Current State-of-The-Art (SoTA) isotro…
Block Coordinate Plug-and-Play Methods for Blind Inverse Problems
Weijie Gan, Shirin Shoushtari, Yuyang Hu +3
Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and lea…
Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems
Zihao Zou, Jiaming Liu, Brendt Wohlberg +1
There has been significant recent interest in the use of deep learning for regularizing imaging inverse problems. Most work in the area has focused on regularization imposed implic…
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan +4
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage i…
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
Junhao Hu, Shirin Shoushtari, Zihao Zou +3
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and…
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Weijie Gan, Chunwei Ying, Parna Eshraghi +7
Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite…
SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction
Yuyang Hu, Weijie Gan, Chunwei Ying +6
Deep model-based architectures (DMBAs) integrating physical measurement models and learned image regularizers are widely used in parallel magnetic resonance imaging (PMRI). Traditi…
Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN
Tomas Kerepecky, Jiaming Liu, Xue Wen Ng +2
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We addr…
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
Shirin Shoushtari, Jiaming Liu, Yuyang Hu +1
There is a growing interest in deep model-based architectures (DMBAs) for solving imaging inverse problems by combining physical measurement models and learned image priors specifi…
Online Deep Equilibrium Learning for Regularization by Denoising
Jiaming Liu, Xiaojian Xu, Weijie Gan +2
Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-used frameworks for solving imaging inverse problems by computing fixed-points of operators combining ph…
Monotonically Convergent Regularization by Denoising
Yuyang Hu, Jiaming Liu, Xiaojian Xu +1
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors. Recent work has reported the state-of-the-a…
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative Mapping
Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu +4
Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and -inhomogeneity-corrected maps…
Deformation-Compensated Learning for Image Reconstruction without Ground Truth
Weijie Gan, Yu Sun, Cihat Eldeniz +3
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has sho…