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Found 48 papers, 12 papers with code
Decoupled Data Consistency with Diffusion Purification for Image Restoration
To solve image restoration problems, many existing techniques achieve data consistency by incorporating additional likelihood gradient steps into the reverse sampling process of diffusion models.
Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction
In this work, we first provide an analysis of how DIP recovers information from undersampled imaging measurements by analyzing the training dynamics of the underlying networks in the kernel regime for different architectures.
Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures
This is due to the necessity of tracking extensive forward and reverse diffusion trajectories, and employing a large model with numerous parameters across multiple timesteps (i. e., noise levels).
Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering
In this work, we propose a novel patient-adaptive MRI sampling algorithm based on grouping scans within a training set.
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
To address this problem, we propose a novel image reconstruction framework, termed Smoothed Unrolling (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning approach.
Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning
In our experiments, we study combinations of supervised deep network reconstructors and MBIR solver with learned sparse representation-based priors or analytical priors.
Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification
In particular, we present a robustification strategy that improves the resilience of DL-based MRI reconstruction methods by utilizing pretrained diffusion models as noise purifiers.
SMUG: Towards robust MRI reconstruction by smoothed unrolling
To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation.
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration
To this end, we propose a novel deep reinforcement learning (DRL) based PnP framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for robust image restoration tasks.
Learning Sparsity-Promoting Regularizers using Bilevel Optimization
We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images.
Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data
In brief, our algorithm alternates between searching for neighbors in a data set that are similar to the test reconstruction, and training a local network on these neighbors followed by updating the test reconstruction.
Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction
Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise.
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction
In this study, we propose a network-structured sparsifying transform learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning.
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
This work focuses on image reconstruction in such settings, i. e., when both the number of available CT projections and the training data is extremely limited.
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)
We present a method for supervised learning of sparsity-promoting regularizers, a key ingredient in many modern signal reconstruction problems.
Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements.
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications.
Comparing One-step and Two-step Scatter Correction and Density Reconstruction in X-ray CT
In this paper, we compare idealized versions of these two approaches with synthetic experiments.
Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction
We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction.
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography
To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it.
Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings.
Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction
In this work, we develop a new image reconstruction approach based on a novel multi-layer model learned in an unsupervised manner by combining both sparse representations and deep models.
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis.
Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration
Experimental results demonstrate that (1) Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed multi-modality image restoration scheme achieves superior denoising results in both efficiency and effectiveness on RGB-NIR images.
Supervised Learning of Sparsity-Promoting Regularizers for Denoising
We present a method for supervised learning of sparsity-promoting regularizers for image denoising.
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction
Signal models based on sparse representation have received considerable attention in recent years.
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms.
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computational imaging applications.
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
The model could be pre-learned from datasets, or learned simultaneously with the reconstruction, i. e., blind CS (BCS).
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability.
Learning Multi-Layer Transform Models
Learned data models based on sparsity are widely used in signal processing and imaging applications.
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements.
SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models
The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
Signal Processing Image and Video Processing Optimization and Control Medical Physics
Analysis of Fast Structured Dictionary Learning
Sparsity-based models and techniques have been exploited in many signal processing and imaging applications.
Analysis of Fast Alternating Minimization for Structured Dictionary Learning
Alternating minimization algorithms have been particularly popular in dictionary or transform learning.
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction.
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images.
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging
For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The $\ell_0$ Method
The proposed block coordinate descent algorithm involves efficient closed-form solutions.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns.
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications
Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision.
Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing
In this work, we focus on blind compressed sensing (BCS), where the underlying sparse signal model is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly undersampled measurements.
$\ell_0$ Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees
Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary.
Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT.
