7 citations · 23 across the 19 of their papers we have counts for
8 papers · 1 filter
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…