In this work, we propose a novel approach to the efficient creation of high-resolution bathymetric charts. An overview of the proposed method is shown in Fig 2. Our basic observation is that a gridded bathymetric chart of a sea area can be treated as a digital image consisting of pixels whose values represent depths. Motivated by this observation, we employ a technique of digital image processing called superresolution, whose aim is to enhance the resolution of images by recovering missing details. In this way, we can effectively obtain fine, high-resolution bathymetric charts from coarse, low-resolution data, which are easier to obtain than high-resolution data. Thus, we can significantly reduce the number of sea areas or points that must be measured, thereby accelerating the detailed mapping of the seafloor and the creation of high-resolution bathymetric charts around the globe. More specifically, we employ a modern superresolution methodology based on deep learning to automatically extract geometric features of bathymetric images, i.e., complex structures like peaks and valleys, and accurately recover them via reconstructing high-resolution images from low-resolution ones. As our experimental results demonstrate, the proposed method is more accurate and thus, more effective than naive interpolation for resolution enhancement both in qualitative and quantitative terms. To sum up, we apply deep-learning-based image superresolution to bathymetry and reveal its effectiveness, which is the main contribution of this work.
AI-based upsampling tech creates high-res versions of low-res images: Digital Photography Review
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