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.
|Conference||2020 Global Oceans|
|Period||05/10/2020 → 14/10/2020|
|Series||Global Oceans 2020: Singapore – U.s. Gulf Coast|
- Single Image Super-Resolution
- Low-bandwidth data transmission
- Deep learning
- Trawl fishing