Curvelet Approach for Deep-sea Sonar Image Denoising, Contrast Enhancement and Fusion

Abstract

AbstractSide-scan sonar acquires high quality imagery of the seafloor with very high spatial resolution but poor locational accuracy. However, multi-beam sonar obtains high precision position and underwater depth in seafloor points. In order to fully utilize all information of these two types of sonars, it is necessary to fuse the two kinds of sonar data. This paper gives curvelet transform for enhancing the signals or details in different scales separately. It also proposes a new intensity sonar image fusion method, which is based on curvelet transform. Considering the sonar image forming principle, for the low frequency curvelet coefficients, we use the maximum local energy method to calculate the energy of two sonar images. For the high frequency curvelet coefficients, we take absolute maximum method as a measurement. The main attribute of this paper is: Firstly, the multi-resolution analysis method is well adapted the cured-singularities and point-singularities. It is useful for sonar intensity image enhancement. Secondly, maximum local energy is well performing the intensity sonar images, which can achieve perfect fusion result. The experimental results show that the method can be used in the flat seafloor or the isotropic seabed. Compared with wavelet transform method, this method can get better performance.Keywords: Curvelet transformSide-scan sonarMulti-beam sonarImage processingDeep-sea terrain detection Additional informationNotes on contributorsHuimin LuHuimin Lu was born in Yangzhou, China, on November, 1985. He received the B.S. degree in Electronics Information Science and Technology from Yangzhou University in 2008. And he received M.S. degrees in Electrical Engineering from Kyushu Institute of Technology and Yangzhou University in 2011, respectively. Recently, he is a Ph.D. candidate in Kyushu Institute of Technology. His current research interests include computer vision, communication and deep-sea information processing. He is a student member of IEEE, IEICE, ACM andJSPS.Akira YamawakiAkira Yamawaki was born in Chiba, Japan, on November, 1974. He received M.S. degrees in Electrical Engineering from Kyushu Institute of Technology, Japan in 1999. During 1999–2000, he was a system-on-chip designer of Mitsubishi Electric Corporation. He received Ph.D. in Electrical and Electronic Engineering from Kyushu Institute of Technology in 2006. From 2000–2007 he was an assistant in Kyushu Institute of Technology. Since 2007, he has been an Assistant Professor in Kyushu Institute of Technology. His current research interests include computer vision, digital hardware system, smart sensor system, sensor network and reconfigurable hardware system. He is a member of IEICE, senior member of IIAE.Seiichi SerikawaSeiichi Serikawa was born in Kumamoto, Japan, on June, 1961. He received the B.S. and M.S. degrees in Electronic Engineering from Kumamoto University in 1984 and 1986. During 1986–1990, he stayed in Tokyo Electron Company. From 1990 to 1994, he was an assistant in Kyushu Institute of Technology. He received the Ph.D. degree in Electronic Engineering from Kyushu Institute of Technology, in 1994. From 1994 to 2000, he was an Assistant Professor at the Kyushu Institute of Technology. From 2000 to 2004, he was an Associate Professor at the Kyushu Institute of Technology. Science 2004, he has been a Professor at the Kyushu Institute of Technology. He worked as Vice President of School of Engineering in Kyushu Institute of Technology from 2010 to 2012. Recently, he is the Dean of Department of Electrical Engineering and Electronics of School of Engineering in Kyushu Institute of Technology. His current research interests include computer vision, sensors, and robotics. He is a member of IEEJ, and IEICE, and the president of IIAE.