Journal
SENSORS
Volume 19, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/s19194190
Keywords
computational imaging; Fourier single-pixel imaging; deep learning
Funding
- National Natural Science Foundation of China (NSFC) [61875012, 61871031]
- Natural Science Foundation of Beijing Municipality [4182058]
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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 x 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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