Search Results for author:
Found 19 papers, 3 papers with code
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion
Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomographic inversion (TomoSAR).
Multi-static Parameter Estimation in the Near/Far Field Beam Space for Integrated Sensing and Communication Applications
The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field assumptions, and is used to obtain a first estimate of the target parameters.
Extended Target Parameter Estimation and Tracking with an HDA Setup for ISAC Applications
We investigate radar parameter estimation and beam tracking with a hybrid digital-analog (HDA) architecture in a multi-block measurement framework using an extended target model.
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography
An emerging technique known as deep unrolling provided a good combination of the descriptive ability of neural networks, explainable, and computational efficiency for BPDN.
A physics-informed neural network framework for modeling obstacle-related equations
Deep learning has been highly successful in some applications.
Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery
Countless signal processing applications include the reconstruction of signals from few indirect linear measurements.
Learning Structured Sparse Matrices for Signal Recovery via Unrolled Optimization
Countless signal processing applications include the reconstruction of an unknown signal from very few indirect linear measurements.
Solving Inverse Problems with Conditional-GAN Prior via Fast Network-Projected Gradient Descent
Our experiments on the MNIST and CelebA datasets show that the combination of measurement conditional model with NPGD works well in recovering the compressed signal while achieving similar or in some cases even better performance along with a much faster reconstruction.
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing
In many scenarios, the number of defects that we are interested in is limited and the signaling response of the layered structure can be modeled as a low-rank structure.
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging
Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites.
Super-Resolution for Doubly-Dispersive Channel Estimation
In this work we consider the problem of identification and reconstruction of doubly-dispersive channel operators which are given by finite linear combinations of time-frequency shifts.
Super-Resolution
Information Theory
Numerical Analysis
Information Theory
Numerical Analysis
47A62, 65R30, 65T99, 94A20
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging
More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery
Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging.
Classification of Spot-welded Joints in Laser Thermography Data using Convolutional Neural Networks
Spot welding is a crucial process step in various industries.
Towards Neurally Augmented ALISTA
It is well-established that many iterative sparse reconstruction algorithms such as ISTA can be unrolled to yield a learnable neural network for improved empirical performance.
Neurally Augmented ALISTA
A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data.
DeepInit Phase Retrieval
This paper shows how data-driven deep generative models can be utilized to solve challenging phase retrieval problems, in which one wants to reconstruct a signal from only few intensity measurements.
