이미지 인페인팅을 활용한 레이다 이미지 노이즈 제거
Noise Removal of Radar Image Using Image Inpainting
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초록
Marine environment analysis and ship motion prediction during ship navigation are important technologies for safe and economical operation of autonomous ships. As a marine environment analysis technology, there is a method of analyzing waves by measuring the sea states through images acquired based on radar(radio detection and ranging) signal. However, in the process of deriving marine environment information from radar images, noises generated by external factors are included, limiting the interpretation of the marine environment. Therefore, image processing for noise removal is required. In this study, image inpainting by partial convolutional neural network model is proposed as a method to remove noises and reconstruct radar images.
Abstract
Marine environment analysis and ship motion prediction during ship navigation are important technologies for safe and economical operation of autonomous ships. As a marine environment analysis technology, there is a method of analyzing waves by measuring the sea states through images acquired based on radar(radio detection and ranging) signal. However, in the process of deriving marine environment information from radar images, noises generated by external factors are included, limiting the interpretation of the marine environment. Therefore, image processing for noise removal is required. In this study, image inpainting by partial convolutional neural network model is proposed as a method to remove noises and reconstruct radar images.
참고문헌 (6)
- Baek, J. Y., "Removal of objects in digital image by double exemplar-based image inpainting", Proceedings of Korean Institute of Information Scientists and Engineers winter Conference, pp.1721~1723 (2016)
- Dankert, H., "Ocean surface determination from X-band radar-image sequences", Journal of Geographical Research, 109(4), pp.1~11 (2004)
- Liu, G., "Image inpainting for irregular holes using partial convolutions", Proceedings of the European Conference on Computer Vision, pp.85~100 (2018)
- Ronneberger, O., "U-net:Convolutional networks for biomedical image segmentation", Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.234~241 (2015)
- Seol, D. I., "Research on the influence of ocean weather on autonomous ship", Proceedings of Korean Society of Marine Environment and Safety biannual meeting, pp.78~78 (2018)
- Yeh, R. A., "Semantic image inpainting with deep generative models", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5485~5493 (2017)
