Accession Number:

AD1028639

Title:

High Resolution Bathymetry Estimation Improvement with Single Image Super-Resolution Algorithm "Super-Resolution Forests"

Descriptive Note:

Technical Report

Corporate Author:

Naval Research Laboratory Washington United States

Personal Author(s):

• Einsidleg,Dylan
• Nock,Kristen
• Bonanno,David
• Elmore,Paul
• Wood,Warren
• Petry,Fred
• Smith,Leslie

Report Date:

2017-01-26

Pagination or Media Count:

13.0

Abstract:

Using the single image super-resolution algorithm Super-Resolution Forests SRF, this paper shows the ability to improve the prediction of high resolution bathymetry data. Borrowing the machine-learning technique of training and testing on a dictionary of sets data, we could create high resolution estimates of bathymetry data similar to estimates typically created with this technique using image data. By implementing a changed variance on the training process of the SRF algorithm, we were able to further increase the mean PSNR score of the high resolution estimated data from previously used bicubic interpolation.

Descriptors:

• high resolution
• machine learning
• bathymetry
• algorithms
• images
• ESTIMATES
• DATABASES

Subject Categories:

• Physical and Dynamic Oceanography
• Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE