Conference paper
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission
Low-bandwidth communication, such as underwater acoustic communication, is limited by best-case data rates of 30–50 kbit/s. This renders such channels unusable or inefficient at best for single image, video, or other bandwidth-demanding sensor-data transmission. To combat data-transmission bottlenecks, we consider practical use-cases within the maritime domain and investigate the prospect of Single Image Super-Resolution methodologies.
This is investigated on a large, diverse dataset obtained during years of trawl fishing where cameras have been placed in the fishing nets. We propose down-sampling images to a low-resolution low-size version of about 1 kB that satisfies underwater acoustic bandwidth requirements for even several frames per second.
A neural network is then trained to perform up-sampling, trying to reconstruct the original image. We aim to investigate the quality of reconstructed images and prospects for such methods in practical use-cases in general. Our focus in this work is solely on learning to reconstruct the high-resolution images on “real-world” data.
We show that our method achieves better perceptual quality and superior reconstruction than generic bicubic up-sampling and motivates further work in this area for underwater applications.
Language: | English |
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Publisher: | IEEE |
Year: | 2020 |
Pages: | 1-6 |
Proceedings: | 2020 Global Oceans |
Journal subtitle: | Singapore – U.s. Gulf Coast |
ISBN: | 1728154464 , 1728184096 , 9781728154466 and 9781728184098 |
Types: | Conference paper |
DOI: | 10.1109/IEEECONF38699.2020.9389122 |
ORCIDs: | Ravn, Ole |