In this paper, we introduce the methodology that utilizes deep learning-based front-end to enhance underwater feature matching. Both optical camera and sonar are widely applicable sensors in underwater research, however, each sensor has its own weaknesses, such as light condition and turbidity for the optic camera, and noise for sonar. To overcome the problems, we proposed the opti-acoustic transformation method. Since feature detection in sonar image is challenging, we converted the sonar image to an optic style image. Maintaining the main contents in the sonar image, CNN-based style transfer method changed the style of the image that facilitates feature detection. Finally, we verified our result using cosine similarity comparison and feature matching against the original optic image.

Abstract
1. 서 론
2. 선행 연구 조사
    2.1 광-음향학(Opti-acoustics)
    2.2 신경망 기반 스타일 변환
    2.3 수중 이미지에의 적용
3. 연구 방법
    3.1 이미지 전처리
    3.2 CNN을 이용한 이미지 특징 추출
    3.3 손실 최소화를 통한 이미지 생성
4. 연구 결과
    4.1 수조 실험 환경
    4.2 신경망 기반 스타일 변환
    4.3 특징점 매칭 평가
    4.4 특징 벡터의 유사성 평가
5. 결 론
References

• 장혜수(Dept. of Civil and Environmental Engineering, KAIST) | Hyesu Jang
• 이영준(Korea Research Institute Ship and Ocean engineering(KRISO)) | Yeongjun Lee
• 김기섭(Dept. of Civil and Environmental Engineering, KAIST) | Giseop Kim
• 김아영(Dept. of Civil and Environmental Engineering, KAIST) | Ayoung Kim Corresponding author