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Perceptual Autoencoder for Compressive Sensing Image Reconstruction
Volume 31, Issue 3 (2020), pp. 561–578
Pub. online: 17 June 2020      Type: Research Article      Open accessOpen Access
Received
1 August 2019
Accepted
1 May 2020
Published
17 June 2020

Abstract

This paper presents a non-iterative deep learning approach to compressive sensing (CS) image reconstruction using a convolutional autoencoder and a residual learning network. An efficient measurement design is proposed in order to enable training of the compressive sensing models on normalized and mean-centred measurements, along with a practical network initialization method based on principal component analysis (PCA). Finally, perceptual residual learning is proposed in order to obtain semantically informative image reconstructions along with high pixel-wise reconstruction accuracy at low measurement rates.

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Biographies

Ralašić Ivan
ivan.ralasic@fer.hr

I. Ralasic received his BSc degree in computing and MSc degree in information and communication technology from the University of Zagreb, in 2014 and 2016, respectively. He is currently pursuing the PhD degree at the University of Zagreb, Faculty of Electrical Engineering and Computing. His current research interests include signal processing, compressive sensing, sparse modelling and machine learning. He is a Student Member of IEEE.

Seršić Damir

D. Seršić received the diploma degree and the MS and PhD degrees in electrical engineering from the University of Zagreb, Zagreb, Croatia, in 1986, 1993, and 1999, respectively. Since 1987, he has been with the Faculty of Electrical Engineering and Computing, University of Zagreb, where he is currently a full professor. His current research interests include theory and applications of wavelets, advanced signal and image processing, adaptive systems, blind source separation, and compressive sensing. Dr. Seršić is a member of the European Association for Signal Processing. From 2006 to 2008, he served as the chair for the Croatian IEEE Signal Processing Chapter.

Šegvić Siniša

S. Šegvić received his PhD degree in computer science, in 2004. He spent one year as a post-doctoral researcher at IRISA/INRIA, Rennes, France, and also at TU Graz, Austria. He is currently a full professor at UniZg-FER. His research and professional interests focus on lightweight convolutional architectures for semantic segmentation, detection, re-identification, outlier detection, and semantic forecasting.


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Copyright
© 2020 Vilnius University
Open access article under the CC BY license.

Keywords
compressive sensing convolutional autoencoder deep learning image reconstruction perceptual loss principal component analysis

Funding
This work was supported in part by the Croatian Science Foundation under Projects IP-2014-09-2625 and IP-2019-04-6703, and in part by the European Regional Development Fund under Grant KK.01.1.1.01.0009 (DATACROSS).

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