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Low-bandwidth communication, such as underwater acoustic communication, islimited by best-case data rates of 30--50 kbit/s. This renders such channelsunusable or inefficient at best for single image, video, or otherbandwidth-demanding sensor-data transmission. To combat data-transmissionbottlenecks, we consider practical use-cases within the maritime domain andinvestigate the prospect of Single Image Super-Resolution methodologies. Thisis investigated on a large, diverse dataset obtained during years of trawlfishing where cameras have been placed in the fishing nets. We proposedown-sampling images to a low-resolution low-size version of about 1 kB thatsatisfies underwater acoustic bandwidth requirements for even several framesper second. A neural network is then trained to perform up-sampling, trying toreconstruct the original image. We aim to investigate the quality ofreconstructed images and prospects for such methods in practical use-cases ingeneral. Our focus in this work is solely on learning to reconstruct thehigh-resolution images on "real-world" data. We show that our method achievesbetter perceptual quality and superior reconstruction than generic bicubicup-sampling and motivates further work in this area for underwaterapplications.

Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission