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Research on Ripple Removal on the Basis of Marine Monitoring

Published: 24 February 2017 Publication History

Abstract

In this thesis, the research status of the frequency characteristic of digital image and sinusoidal noise removal algorithm is overviewed, the relevant frequency characteristic of digital image is analysed on the basis of Fourier transform formulas and properties, the frequency information of image contained in the spectrum is illustrated. Aiming at the properties of the sinusoidal noise model in the frequency domain, the processing peculiarity and result of the common ripple removal algorithm that takes the sinusoidal noise as prototype is researched and compared. The image containing ripple is processed and the image quality is evaluated after processing at marine monitoring. The research results not only show that the image frequency domain filtering can remove the ripple well, but also reflect inevitability of ring effect during the image processing in frequency domain.

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ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing
February 2017
545 pages
ISBN:9781450348171
DOI:10.1145/3055635
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2017

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Author Tags

  1. Ripple removal
  2. Sinusoidal noise
  3. The frequency characteristic of digital image

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  • Research-article
  • Research
  • Refereed limited

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  • National Science Foundation of China (NSFC)

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ICMLC 2017

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