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Dec 16, 2022 · We propose two model-based learning methods, including deep unfolding the denoising process in spectral estimation, and constructing an encoder-decoder deep ...
Jul 18, 2023 · This addresses the application to cal- cium imaging when the sampling kernel is unknown and hence classical methods fail to reconstruct the FRI ...
Abstract—Finite Rate of Innovation (FRI) sampling theory enables reconstruction of classes of continuous non-bandlimited signals that have a small number of ...
Jul 20, 2023 · The code consists of two different learning-based FRI models: Deep Unfolded Projected Wirtinger Gradient Descent (Deep Unfolded PWGD) and FRI ...
Finite Rate of Innovation (FRI) sampling theory enables reconstruction of classes of continuous non-bandlimited signals that have a small number of free ...
[PDF] Learning-Based Reconstruction of FRI Signals - Semantic Scholar
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This work proposes two model-based learning methods, including deep unfolding the denoising process in spectral estimation, and constructing an ...
Jan 1, 2023 · Simulation results of both learning algorithms indicate significant improvements of the breakdown PSNR over classical subspace-based methods.
Learning-Based Reconstruction of FRI Signals | Request PDF
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Apr 27, 2024 · Simulation results of both learning algorithms indicate significant improvements of the breakdown PSNR over classical subspace-based methods.
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This theory is known as Finite Rate of Innovation (FRI). This thesis extends the current theory with applications in neuroscience and sparse vector recovery.
Abstract—Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited signals.