  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.
