BCD-Net uses identical CNN structures for image recovery in undersampled imaging.
problem Challenges in obtaining accurate images from undersampled or noisy measurements.
method Incorporates image mapping CNN into BCD signal recovery method using alternating direction method of multipliers.
result Significantly more accurate image recovery compared to existing methods.
Develops PRPCA for smooth image recovery combining low-rank and smoothness.
problem Image matrix recovery under low-rank and smoothness assumptions.
method Projected Robust PCA (PRPCA) framework combining low-rank and smoothness.
result Explicit statistical guarantees for PRPCA, reducing matrix dimensionality.
Unsupervised image recovery using Stein's Unbiased Risk Estimator.
problem Learning from unlabeled and noisy data.
method Revisiting Stein's Unbiased Risk Estimator (SURE) for image recovery.
result SURE can be used to train CNNs for image denoising and recovery without ground truth data.
Paper proposes a new method for recovering missing samples in images.
problem Missing sample recovery in image signals.
method Iterative sparse recovery algorithm using constrained l1-norm minimization with a new CSIM fidelity metric. result Simulation results demonstrate the efficiency of the proposed method.
New CSIM index improves image patch recovery from missing data.
problem Recovering missing image samples using sparse representation.
method Proposes a new convex similarity index (CSIM) and an iterative sparse recovery method.
result Proves the convergence of the algorithm to the globally optimal solution.
New method proves exact recovery for tensor decomposition under reshuffling.
problem Numerical defects limit practical applications of tensor decomposition.
method Proves exact-recovery property for latent convex tensor decomposition using reshuffling.
result Generalized LCTD achieves exact recovery under reshuffling.
Improved image reconstruction from sparse measurements using generative models.
problem Signal recovery from limited compressed measurements.
method Generative model with constrained latent variables for stable signal reconstruction.
result Improved reconstruction accuracy and preservation of realistic features.
Paper tackles image recovery from blurry measurements using deep generative priors.
problem Jointly recovering two real-valued signals from phaseless circular convolutions.
method Alternating gradient descent algorithm with deep generative priors.
result Reconstructs quality images from blurry measurements.
This paper tackles tensor recovery from noisy and multi-level quantized measurements.
problem Tensors from multi-level quantized measurements.
method Nonconvex optimization problem with alternating proximal gradient descent.
result The recovery error diminishes to zero with increasing tensor dimensions.
A new method precisely recovers latent vectors from GAN-generated images.
problem No out-of-the-box method to reverse GAN mappings.
method Gradient-based stochastic clipping technique.
result Precise recovery of latent vectors 100% of the time for GAN-generated images.
Paper proposes a multi-task model for CECT macromolecule classification, segmentation, and recovery.
problem Challenges in recognizing and recovering macromolecular structures due to structural diversity and imaging limitations.
method A novel multi-task 3D CNN model that shares learned features across tasks.
result Multi-task model outperforms single-task methods and discovers novel structures.
Untrained neural networks can recover natural images from few measurements.
problem Recovering natural images from a small number of measurements.
method Gradient descent on un-trained convolutional neural networks.
result Untrained neural networks can approximate reconstruct signals/images from a near minimal number of random measurements.
Paper discusses new stochastic algorithms for sparse signal recovery.
problem Sparse signal recovery in medical imaging and remote sensing.
method Proposes and analyzes stochastic natural thresholding algorithms.
result Demonstrates improved performance of StoNT algorithms.
Theoretical justification for image inpainting using diffusion models.
problem Improving sample recovery in image inpainting without retraining.
method Analysis of RePaint algorithm and proposing RePaint+ to correct misalignment. result RePaint+ algorithm provably recovers the true sample with linear convergence. A new method trains and samples from energy-based models using diffusion recovery likelihood.
problem Training and sampling high-dimensional datasets with energy-based models is challenging.
method Trains EBMs with a diffusion recovery likelihood method, maximizing conditional probabilities of data at different noise levels.
result Generates high-fidelity images with low FID and inception scores, and accurately estimates normalized data density.
New algorithm recovers signals from low-precision data in interferometry and imaging.
problem Signal loss in data compression for interferometry and medical imaging.
method Normalized Iterative Hard Thresholding with aggressive quantization.
result Recovery guarantees for low-precision data in compressive sensing.
New neural network LDAMP outperforms existing algorithms in image recovery.
problem Challenging problem of fast and accurate compressive image recovery.
method Inspired by D-AMP, developed LDAMP neural network architecture.
result LDAMP outperforms BM3D-AMP and NLR-CS in accuracy and run time.
SMM improves signal recovery from noisy data.
problem Estimating signals from noisy and scaled observations.
method Spiked mixture model (SMM) with EM algorithm.
result SMM outperforms GMM in signal recovery.
Proposes a new tensor grid method for image completion.
problem Image completion from missing data.
method Low-rank tensor grid with two-stage density matrix renormalization group initialization and alternating least squares factorization.
result The proposed tensor grid method outperforms existing methods in image recovery accuracy.
Deep model learns coupled representations from side information for sparse signal recovery.
problem Recovering signals from undersampled, incomplete or noisy linear measurements.
method Deep unfolding model incorporating side information from different modalities.
result Superior performance compared to single-modal and multimodal methods.
New method improves human mesh recovery for obese people.
problem Improving mesh recovery for obese people.
method Generative optimization of mesh parameters from 2D keypoints.
result Significant improvement in mesh recovery performance on obese person images.
New method optimizes MRI sampling patterns for faster scans.
problem Accelerate MRI scans without sacrificing image quality.
method Joint learning of adaptive sampling patterns and model-based recovery.
result Improved MR image quality compared to other methods.
MimicGAN learns to recover images from corrupted data without supervision.
problem Recovering images from corrupted data without known prior information.
method Generative adversarial networks (GANs) to learn corruption mimicking.
result MimicGAN outperforms existing techniques in blind image recovery and adversarial defense.
Improved image recovery with minimal data using untrained neural networks.
problem Solving inverse problems with limited data.
method Pre-training neural networks with a small number of examples to improve performance.
result Performance increases as data increases, matching generative models with less than 1% of training data.
Gradient descent recovers low-rank matrices from corrupted measurements with double over-parameterization.
problem Robust recovery of low-rank matrices from grossly corrupted measurements.
method Gradient descent with discrepant learning rates for double over-parameterized models.
result Gradient descent with discrepant learning rates provably recovers the underlying matrix without prior knowledge on rank or sparsity.
New method recovers compressed crack images for automatic segmentation.
problem Recover crack images from compressed data for SHM systems.
method Generative model-based CS method for crack images.
result Generative model effectively captures crack features for segmentation.
We discuss a general notion of "sparsity structure" and associated recoveries of a sparse signal from its linear image of reduced dimension possibly corrupted with noise. Our approach allows for unified treatment of (a) the "usual sparsity" and "usual ℓ1 recovery," (b) block-sparsity with possibly overlapping blo…
One-dimensional CNNs improve signal recovery from sparse measurements.
problem Recovering signals from limited data.
method One-dimensional Deep Image Prior (DIP) using CNNs with regularization.
result One-dimensional CNNs outperform traditional methods in signal recovery.
The paper validates a method for recovering over-parameterized matrices and images from noisy measurements.
problem Recovering a low-rank matrix from noisy measurements when the rank is unknown.
method Using gradient descent with small random initialization on a nonconvex objective function built from a rank-overspecified factored representation of the matrix variable.
result Gradient descent iterations converge to the ground-truth matrix under certain conditions and can be stopped efficiently to detect a nearly optimal estimator.
3d-SMRnet speeds up MPI system matrix recovery to 1 minute with high quality.
problem Slow system matrix recovery in MPI due to recalibration.
method 3d-System Matrix Recovery Network using deep learning.
result 3d-SMRnet recovers 3d system matrix with 64x subsampling in 1 minute.
Deep learning boosts micro-CT image resolution and texture recovery.
problem Compensating for image resolution trade-offs in micro-CT imaging.
method EDSRGAN trained on a diverse dataset of uCT images.
result EDSRGAN outperforms other methods in texture recovery and resolution.
A variational Bayesian method improves image restoration in Poisson-Gaussian noise.
problem Signal recovery in the presence of non-Gaussian noise, especially Poisson-Gaussian.
method Variational Bayesian framework for estimating posterior distribution, majorization technique for non-Gaussian likelihood.
result The proposed method achieves performance comparable to manually tuned regularization parameters.
This work uses diffusion models for accurate signal recovery from semi-parametric models.
problem Recovering signals from semi-parametric single index models with discontinuous link functions.
method Proposes an efficient reconstruction method using diffusion models that requires one round of sampling and inversion.
result Demonstrates more accurate reconstructions with fewer evaluations compared to competing methods.
Empirical observations of CNN invertibility explained with a mathematical model.
problem Understanding why Convolutional Neural Networks (CNNs) are approximately invertible.
method Developed a mathematical model of sparse signal recovery consistent with random-weight CNNs, connecting to model-based compressive sensing.
result CNNs trained with random weights are consistent with the mathematical model and can be used for reasonable image reconstruction.
We extend GAN latent space projection for Gaussian priors.
problem Non-trivial latent space projection for GANs with Gaussian priors.
method Extend previous techniques to Gaussian priors.
result Demonstrated effectiveness of extended technique.
Convolutional neural networks can regularize inverse problems without training.
problem Solving inverse problems like image recovery from limited data.
method Fixed or parameterized convolutional networks with few parameters.
result Untrained convolutional networks can recover images from few measurements.
New algorithm uses untrained neural networks for image recovery, offering better compression.
problem Using untrained neural networks for image recovery and theoretical guarantees.
method Projected gradient descent scheme for solving linear and non-linear inverse problems.
result The method achieves better compression rates for the same image quality compared to hand-crafted priors.
A GAN-based projector speeds up image recovery in linear inverse problems.
problem Efficiently solving linear inverse problems with convergence guarantees.
method A GAN-based projector trained with projected gradient descent (PGD).
result Guaranteed O(δ) reconstruction error in O(log(1/δ)) steps. The paper improves conditions for unique recovery in homomorphic sensing of subspaces.
problem Unique recovery of points in a linear subspace from their images under linear maps.
method Tighter and simpler conditions for unique recovery in single and subspace arrangement cases, extending to noise stability.
result Conditions for unique recovery in homomorphic sensing are improved and unified.
GANs improve galaxy image recovery beyond deconvolution limits.
problem Limited recovery of galaxy features from noisy, low-resolution images.
method Training a GAN on galaxy images to recover features from degraded data.
result GANs can recover features from degraded images better than simple deconvolution.
Survey of structured low-rank algorithms for MR signal recovery.
problem Recovering multidimensional signals from few non-uniform measurements.
method Structured low-rank matrix completion formulation.
result Performance guarantees and fast algorithms for large-scale MR problems.
PFE embeds images into sparse regions for better segmentation.
problem Image segmentation challenges with slowly varying signals and sparse region boundaries.
method Piecewise Flat Embedding (PFE) using sparse signal recovery theory, L1,p regularization, and Bregman iterations.
result PFE enhances image segmentation performance on multiple datasets.
Study on limits of recovering sparse variables from phaseless measurements.
problem Support recovery in phase retrieval model with noisy phaseless measurements.
method Information-theoretic analysis, considering discrete and Gaussian models, Gaussian measurement matrices.
result Sharp thresholds with near-matching constant factors for sparsity and signal-to-noise ratio in various scaling regimes.
Proposes a new sparse recovery method using generalized error function.
problem Sparse recovery in signal processing and imaging.
method Introduces a penalty function with shape and scale parameters for sparse recovery.
result The method improves MRI reconstruction and is theoretically sound.
This work provides a guaranteed tensor recovery method by combining low-rankness and smoothness priors.
problem Guaranteed tensor recovery with theoretical guarantees for low-rank and smoothness priors.
method Developed a new regularization term that combines low-rankness and smoothness priors, proving exact recovery guarantees.
result Rigorously proved exact recovery guarantees for tensor completion and tensor robust principal component analysis.
Paper proposes a new method for MRI data recovery using bi-linear modeling.
problem Recovering high-fidelity MRI data from dynamic sequences.
method Bi-linear modeling framework for manifold learning and sparse approximation.
result The method improves MRI data recovery over existing techniques.
New algorithm reduces hyperparameter search space using group sparsity.
problem Efficient hyperparameter selection in machine learning.
method Modifies Harmonica algorithm with group-sparse recovery and HyperBand.
result Improves over existing methods like Successive Halving and Random Search.
Robust tensor ring completion improves tensor recovery accuracy and efficiency.
problem Tensor completion sensitivity to sparse components.
method Robust Tensor Ring Completion (RTRC) with weighted nuclear norms and l1 regularization.
result Exact recovery guarantees and superior performance in various tasks.