Seafloor mapping to create bathymetric charts of the oceans is important for various applications. However, making high-resolution bathymetric charts requires measuring underwater depths at many points in sea areas, and thus, is time-consuming and costly. In this work, treating gridded bathymetric data as digital images, we employ the image-processing technique known as superresolution to enhance the resolution of bathymetric charts by estimating high-resolution images from low-resolution ones. Specifically, we use the recently-developed deep-learning methodology to automatically learn the geometric features of ocean floors and recover their details. Through an experiment using bathymetric data around Japan, we confirmed that the proposed method outperforms naive interpolation both qualitatively and quantitatively, observing an eight-dB average improvement in peak signal-to-noise ratio. Deep-learning-based bathymetric image superresolution 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.

海底测绘以创建海洋测深图对于各种应用而言都很重要。但是，制作高分辨率测深图需要在海域的许多点测量水下深度，因此既费时又昂贵。在这项工作中，将栅格化的测深数据作为数字图像处理，我们采用了称为超分辨率的图像处理技术，通过从低分辨率图像中估计高分辨率图像来增强测深图的分辨率。具体来说，我们使用最新开发的深度学习方法来自动学习海床的几何特征并恢复其详细信息。通过使用日本各地的测深数据进行的实验，我们证实了该方法在质量和数量上都优于单纯的内插法，观察到峰值信噪比平均提高了8 dB。基于深度学习的测深图像超分辨率可以显着减少必须测量的海域或点的数量，从而加速海底的详细映射并在全球范围内创建高分辨率测深图。