2 citations · 9 across the 17 of their papers we have counts for
18 papers
Stochastic Generative Plug-and-Play Priors
Chicago Y. Park, Edward P. Chandler, Yuyang Hu +4
Plug-and-play (PnP) methods are widely used for solving imaging inverse problems by incorporating a denoiser into an optimization algorithm. Score-based diffusion models (SBDMs) ha…
A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Models
Weijie Gan, Xucheng Wang, Tongyao Wang +6
Image reconstruction and image synthesis are important for handling incomplete multimodal imaging data, but existing methods require various task-specific models, complicating trai…
Generative Manifold Distillation: Aligning Restoration Trajectories with Natural Image Prior
Yuyang Hu, Mojtaba Sahraee-Ardakan, Arpit Bansal +4
Pre-trained image restoration models often fail on out-of-distribution (OOD) real-world degradations. Adapting to these domains is challenging as real-world data lacks paired groun…
Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation
Tingjun Liu, Chicago Y. Park, Yuyang Hu +2
Diffusion-based inverse problem solvers (DIS) have recently shown outstanding performance in compressed-sensing parallel MRI reconstruction by combining diffusion priors with physi…
Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restoration
Yuyang Hu, Kangfu Mei, Mojtaba Sahraee-Ardakan +3
Diffusion models show promise for image restoration, but existing methods often struggle with inconsistent fidelity and undesirable artifacts. To address this, we introduce Kernel…
Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal
Yuyang Hu, Suhas Lohit, Ulugbek S. Kamilov +1
Deep learning has achieved some success in addressing the challenge of cloud removal in optical satellite images, by fusing with synthetic aperture radar (SAR) images. Recently, di…
A Self-supervised Diffusion Bridge for MRI Reconstruction
Harry Gao, Weijie Gan, Yuyang Hu +2
Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions. Training traditional DBs for image reco…
Plug-and-Play Priors as a Score-Based Method
Chicago Y. Park, Yuyang Hu, Michael T. McCann +3
Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-ba…
ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction
Yuyang Hu, Albert Peng, Weijie Gan +1
Diffusion bridges (DB) have emerged as a promising alternative to diffusion models for imaging inverse problems, achieving faster sampling by directly bridging low- and high-qualit…
Stochastic Deep Restoration Priors for Imaging Inverse Problems
Yuyang Hu, Albert Peng, Weijie Gan +3
Deep neural networks trained as image denoisers are widely used as priors for solving imaging inverse problems. While Gaussian denoising is thought sufficient for learning image pr…
DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model
Yuyang Hu, Satya V. V. N. Kothapalli, Weijie Gan +5
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model. DiffGEPCI can synthesize h…
Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuning
Chicago Y. Park, Weijie Gan, Zihao Zou +3
Model-based deep learning (MBDL) is a powerful methodology for designing deep models to solve imaging inverse problems. MBDL networks can be seen as iterative algorithms that estim…
A Restoration Network as an Implicit Prior
Yuyang Hu, Mauricio Delbracio, Peyman Milanfar +1
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging. In this work, we introduce a generalization of these methods that allows any image re…
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…
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…
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…
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…