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

Jesper Haahr Christensen, Lars Valdemar Mogensen, Ole Ravn

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of 2020 Global Oceans
Publication date2020
ISBN (Print)9781728154466
Publication statusPublished - 2020
Event2020 Global Oceans - Virtual event
Duration: 5 Oct 202014 Oct 2020


Conference2020 Global Oceans
LocationVirtual event
SeriesGlobal Oceans 2020: Singapore – U.s. Gulf Coast


  • Single Image Super-Resolution
  • Low-bandwidth data transmission
  • Deep learning
  • Trawl fishing


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