CSIE-M: Compressive Sensing Image Enhancement Using Multiple Reconstructed Signals for Internet of Things Surveillance Systems

Artificial intelligence of things has brought artificial intelligence to the cutting-edge Internet of Things. In recent years, compressive sensing (CS), which relies on sparsity, is widely embedded and expected to bring more energy efficiency and a longer battery lifetime to IoT devices. Different f...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2022-02, Vol.18 (2), p.1271-1281
Main Authors: Pham, Chi Do-Kim, Yang, Jian, Zhou, Jinjia
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Cameras
Compressed sensing
Compressive sensing (CS)
Decoding
deep learning approach for compressed image enhancement
Image coding
Image compression
Image enhancement
Image reconstruction
Internet of Things
Machine learning
Modules
multiple-to-one mapping
Noise levels
Quality assessment
Service life assessment
Surveillance systems
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Summary:Artificial intelligence of things has brought artificial intelligence to the cutting-edge Internet of Things. In recent years, compressive sensing (CS), which relies on sparsity, is widely embedded and expected to bring more energy efficiency and a longer battery lifetime to IoT devices. Different from the other image compression standards, CS can get various reconstructed images by applying different reconstruction algorithms on coded data. Using this property, it is the first time to propose a deep learning based compressive sensing image enhancement framework using multiple reconstructed signals (CSIE-M). In this article, first, images are reconstructed by different CS reconstruction algorithms. Second, reconstructed images are assessed and sorted by a no-reference quality assessment module before being input to the quality enhancement module by order of quality scores. Finally, a multiple-input recurrent dense residual network is designed for exploiting and enriching the useful information from the reconstructed images. Experimental results show that CSIE-M obtains 1.88-8.07 dB peek-signal-to-noise (PSNR) improvement while the state-of-the-art works achieve a 1.69-6.69 dB PSNR improvement under sampling rates from 0.125 to 0.75. On the other hand, using multiple reconstructed versions of the signal can improve 0.19-0.23 dB PSNR, and only 4% reconstructing time is increasing compared to using a reconstructed signal.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3082498