Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI,...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 19; no. 19; p. 4190
Main Authors: Rizvi, Saad, Cao, Jie, Zhang, Kaiyu, Hao, Qun
Format: Journal Article
Language:English
Published: Basel MDPI AG 27.09.2019
MDPI
Subjects:
computational imaging
Deep learning
fourier single-pixel imaging
Fourier transforms
Sensors
Signal to noise ratio
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19194190