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Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
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Sensors 2020, 20(3), 594; https://doi.org/10.3390/s20030594
Received: 20 December 2019 / Revised: 13 January 2020 / Accepted: 20 January 2020 / Published: 21 January 2020
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (ResCNN) for reconstructing the spectrum from the compressed measurements. The proposed ResCNN comprises learnable layers and a residual connection between the input and the output of these learnable layers. The ResCNN is trained using both synthetic and measured spectral datasets. The results demonstrate that ResCNN shows better spectral recovery performance in terms of average root mean squared errors (RMSEs) and peak signal to noise ratios (PSNRs) than existing approaches such as the sparse recovery methods and the spectral recovery using CNN. Unlike sparse recovery methods, ResCNN does not require a priori knowledge of a sparsifying basis nor prior information on the spectral features of the dataset. Moreover, ResCNN produces stable reconstructions under noisy conditions. Finally, ResCNN is converged faster than CNN. View Full-Text
Keywords: spectroscopy; compressed sensing; deep learning; inverse problems; sparse recovery; dictionary learning spectroscopy; compressed sensing; deep learning; inverse problems; sparse recovery; dictionary learning
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Kim, C.; Park, D.; Lee, H.-N. Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network. Sensors 2020, 20, 594.

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