Abstract
Side-scan sonar is widely used in maritime search and rescue, submarine geological exploration, target detection and identification, etc. with its long range of action, comprehensive coverage, and large amount of collected data. Image denoising is the premise of the correct interpretation of side-scan sonar images. In this paper, a new weighted kernel function is proposed on the basis of the original Non-Local Means (NLM) denoising algorithm, which leads to an improved non-local mean denoising algorithm. By experimenting with Gaussian white noise images with different noise levels, it appears that the efficiency of this algorithm is superior to that of the NLM algorithm, and the denoising performance is significantly enhanced compared with the NLM and Fast Non-Local Means (FNLM) algorithm, especially for images under solid noise.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
