High-quality image compressed sensing and reconstruction with multi-scale dilated convolutional neural network
Deep learning (DL)-based compressed sensing (CS) has been applied for better
performance of image reconstruction than traditional CS methods. However, most existing …
performance of image reconstruction than traditional CS methods. However, most existing …
Tradeoffs between convergence speed and reconstruction accuracy in inverse problems
Solving inverse problems with iterative algorithms is popular, especially for large data. Due
to time constraints, the number of possible iterations is usually limited, potentially affecting …
to time constraints, the number of possible iterations is usually limited, potentially affecting …
Cascade neural network-based joint sampling and reconstruction for image compressed sensing
Most deep learning-based compressed sensing (DCS) algorithms adopt a single neural
network for signal reconstruction and fail to jointly consider the influences of the sampling …
network for signal reconstruction and fail to jointly consider the influences of the sampling …
An improved structural health monitoring method utilizing sparse representation for acoustic emission signals in rails
The structural health of rails significantly impacts the safety of railway transport, which
requires long-term and accurate monitoring. An improved structural health monitoring (SHM) …
requires long-term and accurate monitoring. An improved structural health monitoring (SHM) …
Back-projection based fidelity term for ill-posed linear inverse problems
Ill-posed linear inverse problems appear in many image processing applications, such as
deblurring, super-resolution and compressed sensing. Many restoration strategies involve …
deblurring, super-resolution and compressed sensing. Many restoration strategies involve …
An adaptive parameter optimization algorithm for simultaneous identification of force location and history with sparse calibration array
An adaptive parameter optimization algorithm (APOA) is proposed for simultaneous force
history and location identification in this work. Because extensive calibrations are time …
history and location identification in this work. Because extensive calibrations are time …
Convergence on Thresholding-Based Algorithms for Dictionary-Sparse Recovery
Abstract We study\(l_0\)-synthesis/analysis methods and the thresholding-based algorithms
for the dictionary-sparse recovery from a few linear measurements perturbed with Gaussian …
for the dictionary-sparse recovery from a few linear measurements perturbed with Gaussian …
Efficient least residual greedy algorithms for sparse recovery
G Leibovitz, R Giryes - IEEE Transactions on Signal Processing, 2020 - ieeexplore.ieee.org
We present a novel stagewise strategy for improving greedy algorithms for sparse recovery.
We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both …
We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both …
Sparse Recovery for Overcomplete Frames: Sensing Matrices and Recovery Guarantees
Signal models formed as linear combinations of few atoms from an over-complete dictionary
or few frame vectors from a redundant frame have become central to many applications in …
or few frame vectors from a redundant frame have become central to many applications in …
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing
Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single
neural network for signal reconstruction, and fail to jointly consider the influences of the …
neural network for signal reconstruction, and fail to jointly consider the influences of the …