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An Adaptive Denoising and Detection Approach for Underwater Sonar Image
- Source :
- Remote Sensing, Vol 11, Iss 4, p 396 (2019), Remote Sensing; Volume 11; Issue 4; Pages: 396
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- An adaptive approach is proposed to denoise and detect the underwater sonar image in this paper. Firstly, to improve the denoising performance of non-local spatial information in the underwater sonar image, an adaptive non-local spatial information denoising method based on the golden ratio is proposed. Then, a new adaptive cultural algorithm (NACA) is proposed to accurately and quickly complete the underwater sonar image detection in this paper. Concretely, NACA has two improvements. In the first place, to obtain better initial clustering centres, an adaptive initialization algorithm based on data field (AIA-DF) is proposed in this paper. Secondly, in the belief space of NACA, a new update strategy is adopted to update cultural individuals in terms of the quantum-inspired shuffled frog leaping algorithm (QSFLA). The experimental results show that the proposed denoising method in this paper can effectively remove relatively large and small filtering degree parameters and improve the denoising performance to some extent. Compared with other comparison algorithms, the proposed NACA can converge to the global optimal solution within small epochs and accurately complete the object detection, having better effectiveness and adaptability.
- Subjects :
- Computer science
Noise reduction
Science
Data field
underwater sonar image
adaptive denoising
detection
adaptive initialization
Initialization
02 engineering and technology
Sonar
0202 electrical engineering, electronic engineering, information engineering
Computer vision
Underwater
Cluster analysis
Cultural algorithm
business.industry
020206 networking & telecommunications
Object detection
Computer Science::Computer Vision and Pattern Recognition
General Earth and Planetary Sciences
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 11
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....41e8df8a277680f3c58c9bd229e8bdaf