XCycles Backprojection Acoustic Super-Resolution

The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-05, Vol.21 (10), p.3453
Main Authors: Almasri, Feras, Vandendriessche, Jurgen, Segers, Laurent, da Silva, Bruno, Braeken, An, Steenhaut, Kris, Touhafi, Abdellah, Debeir, Olivier
Format: Article
Language:English
Subjects:
acoustic camera
Acoustic imaging
Acoustic mapping
Acoustics
Cameras
Computer vision
Data acquisition
Datasets
Deep learning
delay-and-sum beamformer
Embedded systems
Error correction
Error correction & detection
Error reduction
Field programmable gate arrays
Image contrast
Interpolation
Localization
Noise
Sampling error
Sensors
Sound waves
super-resolution
Visible spectrum
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Summary:The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21103453