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2023 Vol.11, Issue 2 Preview Page

Research Article

Underwater Image Enhancement Method Through Visibility Distortion Correction Based on Analysis of Underwater Environment Characteristics

수중 환경 특성 분석 기반의 가시성 왜곡 보정을 통한 수중 영상 복원 기법

Do-Kyung Shin1, Young-Dae Kim2

신도경1, 김영대2

1,2Chief Research Engineer, Maritime Surface Sonar System LIG Nex1
1,2엘아지 넥스원 해양연구소 소나체계개발단 수석연구원
30 June 2023. pp. 67-92
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Abstract
최근 민수 및 국방 분야에서 해저 자원 탐사, 재난 예측, 해저 지형 조사, 대잠 감시정찰, 기뢰 제거 등을 수행하기 위한 무인잠수정의 활용도가 높아지고 있다. 무인잠수정을 활용한 다양한 임무를 수행하기 위해서 수중 광학 영상의 활용도 또한 높아지고 있다. 하지만 수중에서 획득된 영상은 수중으로 전파되는 빛의 감쇠로 인해 색상 왜곡, 낮은 대조비, 수중 안개 현상 및 블러 등의 왜곡이 발생함에 따라 가시성 낮은 문제점이 존재한다. 따라서 본 논문에서는 수중 환경 특성 분석을 통해 왜곡 현상을 제거하고 보정함으로써 영상의 품질을 향상시킨다. 제안한 알고리즘의 시각적 평가 및 정량적 평가를 수행하기 위해서 UIEB 수중 영상 향상 벤치마크 데이터 셋을 사용하였으며, 12개의 기존 방법과의 품질 점수를 비교를 수행하였다. 또한 보정된 영상의 품질 점수를 측정하기 위해서 참조 영상 없이 단일 영상으로 측정하는 무참조 기반의 UIQM, UCIQE, FDUM, CCF, FADE 수중 영상 품질 평가 메트릭을 사용하였다. 실험 결과, 본 논문에서 제안하는 방법이 기존의 수중 영상 개선 방법보다 우수한 성능을 보임을 확인하였다.
Recently, the utilization of unmanned submersibles is increasing in the civil and national defense fields to perform underwater resource exploration, disaster prediction, underwater terrain survey, anti-submarine surveillance and reconnaissance, and mine removal. The use of underwater optical imaging is also increasing in order to perform various missions using unmanned submersibles. However, images acquired underwater have problems with low visibility due to distortions such as color distortion, low contrast ratio, underwater fog, and blur due to attenuation of light propagating underwater. Therefore, in this paper, the quality of the image is improved by removing and correcting the distortion phenomenon through the analysis of the characteristics of the underwater environment. To perform visual evaluation and quantitative evaluation of the proposed algorithm, UIEB underwater image enhancement benchmark data set was used, and quality scores were compared with 12 existing methods. In addition, to measure the quality score of the corrected image, we used UIQM, UCIQE, FDUM, CCF, and FADE underwater image quality evaluation metrics based on non-reference, which measure a single image without a reference image. As a result of the experiment, it was confirmed that the method proposed in this paper showed better performance than the existing underwater image improvement method.
Keywords
Underwater image enhancement
UIEB dataset
Underwater Image Quality Assessment(UIQA)
Underwater fog
Underwater sandy dust
White balance
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Information
  • Publisher :The Society of Convergence Knowledge
  • Publisher(Ko) :융복합지식학회
  • Journal Title :The Society of Convergence Knowledge Transactions
  • Journal Title(Ko) :융복합지식학회논문지
  • Volume : 11
  • No :2
  • Pages :67-92
  • DOI :https://doi.org/10.22716/sckt.2023.11.2.017
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