RepDNet: A re-parameterization despeckling network for autonomous underwater side-scan sonar imaging with prior-knowledge customized convolution

• Published in: Defence technology Vol. 35; no. 5; pp. 259 - 274
• Main Authors: Li, Zhuoyi, Wang, Zhisen, Chen, Deshan, Yip, Tsz Leung, Teixeira, Angelo P.
• Format: Journal Article
• Language: English
• Published: Beijing Elsevier B.V 01.05.2024; KeAi Publishing Communications Ltd; School of Transportation and Logistics Engineering,Wuhan University of Technology,China%State Key Laboratory of Maritime Technology and Safety,Wuhan University of Technology,China; Department of Logistics and Maritime Studies,The Hong Kong Polytechnic University,China%State Key Laboratory of Maritime Technology and Safety,Wuhan University of Technology,China; National Engineering Research Center for Water Transport Safety,Wuhan University of Technology,China%Centre for Marine Technology and Ocean Engineering(CENTEC),Instituto Superior Técnico,Universidade de Lisboa,Portugal; School of Transportation and Logistics Engineering,Wuhan University of Technology,China; KeAi Communications Co., Ltd
• Subjects: Acoustics; Artificial neural networks; Autonomous underwater vehicles; Datasets; Deep learning; Domain knowledge; Energy consumption; Image processing; Inference; Methods; Noise; Noise reduction; Ocean bottom; Ocean floor; Parameterization; Re-parameterization; Side scan sonar; Signal processing; Smoothing; Sonar; Sonar image despeckling; Underwater acoustics; Underwater exploration; Sonar image despeckling; Domain knowledge; Re-parameterization; Side-scan sonar

Side-scan sonar (SSS) is now a prevalent instrument for large-scale seafloor topography measurements, deployable on an autonomous underwater vehicle (AUV) to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory. However, SSS images often suffer from speckle n...