# Matching Underwater Sonar Images by the Learned Descriptor Based on Style Transfer Method

arXiv: https://arxiv.org/abs/2108.12072 · HTML: https://mlsanity.com/paper/2108.12072/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Controlling Perceptual Factors in Neural Style Transfer](https://mlsanity.com/paper/1611.07865/) (2016)
- [Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT](https://mlsanity.com/paper/1405.5769/) (2014)

## Built on by (most cited first, 1 of 1)

- [Towards Generating Realistic Underwater Images](https://mlsanity.com/paper/2505.14296/) (2025)

## Abstract (arXiv)

This paper proposes a method that combines the style transfer technique and the learned descriptor to enhance the matching performances of underwater sonar images. In the field of underwater vision, sonar is currently the most effective long-distance detection sensor, it has excellent performances in map building and target search tasks. However, the traditional image matching algorithms are all developed based on optical images. In order to solve this contradiction, the style transfer method is used to convert the sonar images into optical styles, and at the same time, the learned descriptor with excellent expressiveness for sonar images matching is introduced. Experiments show that this method significantly enhances the matching quality of sonar images. In addition, it also provides new ideas for the preprocessing of underwater sonar images by using the style transfer approach.