Compressive sensing in electromagnetics-a review
Several problems arising in electromagnetics can be directly formulated or suitably recast for
an effective solution within the compressive sensing (CS) framework. This has motivated a …
an effective solution within the compressive sensing (CS) framework. This has motivated a …
Computational methods for sparse solution of linear inverse problems
The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …
linear combination of a few elementary signals drawn from a fixed collection. This paper …
Robust compressed sensing mri with deep generative priors
Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …
Wire: Wavelet implicit neural representations
Implicit neural representations (INRs) have recently advanced numerous vision-related
areas. INR performance depends strongly on the choice of activation function employed in …
areas. INR performance depends strongly on the choice of activation function employed in …
Deep learning techniques for inverse problems in imaging
Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …
wide variety of inverse problems arising in computational imaging. We explore the central …
Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
[图书][B] Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz - 2022 - books.google.com
Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
[图书][B] Dynamic mode decomposition: data-driven modeling of complex systems
The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …
Compressed sensing using generative models
The goal of compressed sensing is to estimate a vector from an underdetermined system of
noisy linear measurements, by making use of prior knowledge on the structure of vectors in …
noisy linear measurements, by making use of prior knowledge on the structure of vectors in …