论文标题
针对特定域特异性超低带宽图像传输的单图像超分辨率
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission
论文作者
论文摘要
低频道通信，例如水下声学通信，受到30--50 kbit/s的最佳数据速率的限制。这使此类频道最多可用于单张图像，视频或其他带宽的传感器数据传输。为了打击数据传输瓶颈，我们考虑了海上域内的实际用例，并研究了单像超分辨率方法的前景。这是对在拖网捕捞几年中获得的大型，多样化的数据集进行了研究的。我们向低分辨率的低尺寸版本的大约1 KB提出了向下采样图像，该版本满足水下声学带宽的要求，即使每秒几个帧也是如此。然后，对神经网络进行了训练，可以进行上采样，并试图重建原始图像。我们旨在调查通常在实际用例中的重建图像和前景的质量。我们对这项工作的重点仅仅是学习重建“现实世界”数据上的高分辨率图像。我们表明，我们的方法比通用的双色上采样可实现更好的感知质量和优越的重建，并激发了该领域的水下应用中进一步的工作。
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.
