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Found 22 papers, 4 papers with code
RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging
In particular, in the case of dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed.
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling
In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time.
Learning to Bound: A Generative Cramér-Rao Bound
The Cram\'er-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems.
Scatter Correction in X-ray CT by Physics-Inspired Deep Learning
Different from previous works, they incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it, and use a deep neural network architecture and cost function, both specifically tailored to the problem.
Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering
We believe that the proposed velocity filtering method has the potential to pave the way to clinical translation of ULM.
Joint Dimensionality Reduction for Separable Embedding Estimation
In this paper, we propose a supervised dimensionality reduction method that learns linear embeddings jointly for two feature vectors representing data of different modalities or data from distinct types of entities.
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems
Furthermore, we relate the denoising performance improvement by combining multiple models, to the image model relationships.
Improving Robustness of Deep-Learning-Based Image Reconstruction
We find that a linear network using the proposed min-max learning scheme indeed converges to the same solution.
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).
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems
A Generative Adversarial Network (GAN) with generator $G$ trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems.
Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere
Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from convolutional measurements $y_i=x_i \circledast f$ ($i=1, 2,\dots, N$).
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling
Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image / video processing applications.
Multichannel Sparse Blind Deconvolution on the Sphere
Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from their circular convolution $y_i=x_i \circledast f$ ($i=1, 2,\dots, N$).
Learning Filter Bank Sparsifying Transforms
We show that many existing transform and analysis sparse representations can be viewed as filter banks, thus linking the local properties of patch-based model to the global properties of a convolutional model.
Blind Gain and Phase Calibration via Sparse Spectral Methods
We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e. g., with noisy measurement or with a bad initial estimate.
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.
Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising
In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT.
Joint Dimensionality Reduction for Two Feature Vectors
This paper addresses the joint dimensionality reduction of two feature vectors in supervised learning problems.
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.
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.
$\ell_0$ 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.
