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Found 47 papers, 11 papers with code
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Our second set of numerical results considers a simple and effective domain adaption strategy that closes the performance gap due to the use of mismatched denoisers.
Block Coordinate Plug-and-Play Methods for Blind Inverse Problems
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 learned image denoisers.
Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems
ELDER is based on a regularization functional parameterized by a CNN and a deep equilibrium learning (DEQ) method for training the functional to be MSE-optimal at the fixed points of the reconstruction algorithm.
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine.
DOLPH: Diffusion Models for Phase Retrieval
Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements.
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors.
SINCO: A Novel structural regularizer for image compression using implicit neural representations
Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression.
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters.
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
SPICE: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane.
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.
Online Deep Equilibrium Learning for Regularization by Denoising
However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.
Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.
Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging
Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models.
Learning Cross-Video Neural Representations for High-Quality Frame Interpolation
This paper considers the problem of temporal video interpolation, where the goal is to synthesize a new video frame given its two neighbors.
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Monotonically Convergent Regularization by Denoising
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.
Bregman Plug-and-Play Priors
Our new Bregman Proximal Gradient Method variant of PnP (PnP-BPGM) and Bregman Steepest Descent variant of RED (RED-BSD) replace the traditional updates in PnP and RED from the quadratic norms to more general Bregman distance.
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping
LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.
Deformation-Compensated Learning for Image Reconstruction without Ground Truth
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
Joint Reconstruction and Calibration using Regularization by Denoising
Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.
Deep Image Reconstruction using Unregistered Measurements without Groundtruth
One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.
Scalable Plug-and-Play ADMM with Convergence Guarantees
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.
Infusing Learned Priors into Model-Based Multispectral Imaging
We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements.
Online Regularization by Denoising with Applications to Phase Retrieval
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.
Block Coordinate Regularization by Denoising
In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables.
Plug-In Stochastic Gradient Method
Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm.
Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm.
Image Restoration using Total Variation Regularized Deep Image Prior
In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.
An Online Plug-and-Play Algorithm for Regularized Image Reconstruction
The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.
signProx: One-Bit Proximal Algorithm for Nonconvex Stochastic Optimization
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for parallel and distributed processing of large datasets.
Stability of Scattering Decoder For Nonlinear Diffractive Imaging
The problem of image reconstruction under multiple light scattering is usually formulated as a regularized non-convex optimization.
Sparse Blind Deconvolution for Distributed Radar Autofocus Imaging
Common techniques that attempt to resolve the antenna ambiguity generally assume an unknown gain and phase error afflicting the radar measurements.
Efficient and accurate inversion of multiple scattering with deep learning
Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography.
Learning-based Image Reconstruction via Parallel Proximal Algorithm
In the past decade, sparsity-driven regularization has led to advancement of image reconstruction algorithms.
Accelerated Image Reconstruction for Nonlinear Diffractive Imaging
The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications.
Online Convolutional Dictionary Learning for Multimodal Imaging
Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems.
SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering
Specifically, it corresponds to a series expansion of the scattered wave with an accelerated-gradient method.
Compressive Imaging with Iterative Forward Models
We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements.
A Recursive Born Approach to Nonlinear Inverse Scattering
The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects.
Depth Superresolution using Motion Adaptive Regularization
Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras.
Learning optimal nonlinearities for iterative thresholding algorithms
Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems.
