Rethinking the CSC model for natural images
Sparse representation with respect to an overcomplete dictionary is often used when
regularizing inverse problems in signal and image processing. In recent years, the …
regularizing inverse problems in signal and image processing. In recent years, the …
Double stochastic resonance induced by varying potential-well depth and width
The role of potential-well depth and width on stochastic resonance (SR) driven by colored
noise with different noise correlation times is explored and evaluated by deriving the analytic …
noise with different noise correlation times is explored and evaluated by deriving the analytic …
Signal estimation and filtering from quantized observations via adaptive stochastic resonance
Using a gradient-based algorithm, we investigate signal estimation and filtering in a large-
scale summing network of single-bit quantizers. Besides adjusting weights, the proposed …
scale summing network of single-bit quantizers. Besides adjusting weights, the proposed …
Barycenters of natural images constrained wasserstein barycenters for image morphing
Image interpolation, or image morphing, refers to a visual transition between two (or more)
input images. For such a transition to look visually appealing, its desirable properties are (i) …
input images. For such a transition to look visually appealing, its desirable properties are (i) …
[HTML][HTML] Training threshold neural networks by extreme learning machine and adaptive stochastic resonance
Threshold neural networks are highly useful in engineering applications due to their ease of
hardware implementation and low computational complexity. However, such threshold …
hardware implementation and low computational complexity. However, such threshold …
Support recovery with Projected Stochastic Gates: Theory and application for linear models
Consider the problem of simultaneous estimation and support recovery of the coefficient
vector in a linear data model with additive Gaussian noise. We study the problem of …
vector in a linear data model with additive Gaussian noise. We study the problem of …
Support recovery with stochastic gates: Theory and application for linear models
Consider the problem of simultaneous estimation and support recovery of the coefficient
vector in a linear data model with additive Gaussian noise. We study the problem of …
vector in a linear data model with additive Gaussian noise. We study the problem of …
Image denoising using convolutional sparse coding network with dry friction
Y Zhang, X Wang, F Wang… - Proceedings of the Asian …, 2022 - openaccess.thecvf.com
Convolutional sparse coding model has been successfully used in some tasks such as
signal or image processing and classification. The recently proposed supervised …
signal or image processing and classification. The recently proposed supervised …
Distributed Bayesian vector estimation using noise-optimized low-resolution sensor observations
The distributed Bayesian vector parameter estimation problem based on low-resolution
observations is investigated in a network, where each node represents an ensemble of …
observations is investigated in a network, where each node represents an ensemble of …
Randomly aggregated least squares for support recovery
O Lindenbaum, S Steinerberger - Signal Processing, 2021 - Elsevier
We study the problem of exact support recovery: given an (unknown) vector θ*∈{− 1, 0, 1} D
with known sparsity k=∥ θ*∥ 0, we are given access to the noisy measurement y= X θ*+ ω …
with known sparsity k=∥ θ*∥ 0, we are given access to the noisy measurement y= X θ*+ ω …