Convolutional Neural Networks for Noniterative Reconstruction of Compressively Sensed Images

Volume: 4, Issue: 3, Pages: 326 - 340
Published: Jun 11, 2018
Abstract
Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this paper, we propose a data-driven noniterative algorithm to overcome the shortcomings of earlier iterative algorithms. Our solution, null ReconNet , is a deep neural network, which is learned end-to-end to map block-wise compressive measurements of the scene to the desired image blocks. Reconstruction of an...
Paper Details
Title
Convolutional Neural Networks for Noniterative Reconstruction of Compressively Sensed Images
Published Date
Jun 11, 2018
Volume
4
Issue
3
Pages
326 - 340
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