High-resolution bathymetry by deep-learning-based image superresolution.
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 d...
Main Authors: | Motoharu Sonogashira, Michihiro Shonai, Masaaki Iiyama |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2020-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0235487 |
Similar Items
-
High-Resolution Bathymetry by Deep-Learning Based Point Cloud Upsampling
by: Naoya Irisawa, et al.
Published: (2024-01-01) -
Towards Open-Set Scene Graph Generation With Unknown Objects
by: Motoharu Sonogashira, et al.
Published: (2022-01-01) -
Deep learning acceleration of multiscale superresolution localization photoacoustic imaging
by: Jongbeom Kim, et al.
Published: (2022-05-01) -
Hyperspectral Image Superresolution via Subspace-Based Deep Prior Regularization
by: Jianwei Zheng, et al.
Published: (2023-01-01) -
Applying single-image super-resolution for the enhancement of deep-water bathymetry
by: Kristen Nock, et al.
Published: (2019-10-01)