The Two-Dimensional Wavelet Transform De-noising and Combining with Side Scan Sonar Image
Two-Dimensional Wavelet Transform De-noising
Abstract
This paper puts forward an image de-noising method based on 2D wavelet transform with the application of the method in seabed identification data collection system. Two-dimensional haar wavelets in image processing presents a unified framework for wavelet image compression and combining with side scan sonar image. Seabed identification target have 7 target detection in side scan sonar imagery result. The vibration signals were analyzed to perform fault diagnosis. The obtained signal was time-domain signal. The experiment result shows that the application of 2D wavelet transform image de-noising algorithm can achieve good subjective and objective image quality and help to collect high quality data and analyze the images for the data center with optimum effects, the features from time-domain signal were extracted. 3 vectors were formed which are v1, v2, v3. In Haar wavelet retained energy is 93.8 %, so from the results, it has been concluded that Haar wavelet transform shows the best results in terms of Energy from De-noised Image processing with side scan sonar imagery.
Downloads
References
2. Lubis, M. Z., Anurogo, W., Khoirunnisa, H., Irawan, S., Gustin, O., & Roziqin, A. (2017). Using Side-Scan Sonar instrument to Characterize and map of seabed identification target in punggur sea of the Riau Islands, Indonesia. Journal of Geoscience, Engineering, Environment, and Technology, 2(1), 1-8.
3. Smith, S. J., & Friedrichs, C. T. (2015). Image processing methods for in situ estimation of cohesive sediment floc size, settling velocity, and density. Limnology and Oceanography: Methods, 13(5), 250-264.
4. Yin, L., Chen, D., & Li, C. (2013). Two-Dimensional Wavelet Transform De-noising Algorithm in Collecting Intelligent Agriculture Image. JSW, 8(4), 893-899.
5. Lewis, Q. W., & Rhoads, B. L. (2015). Resolving two‐dimensional flow structure in rivers using large‐scale particle image velocimetry: An example from a stream confluence. Water Resources Research, 51(10), 7977-7994.
6. Afonso, M., & Sanches, J. M. (2015). Image reconstruction under multiplicative speckle noise using total variation. Neurocomputing, 150, 200-213.
7. Sukanya, Y., & Preethi, J. (2013). Analysis of image compression algorithms using wavelet transform with GUI in Matlab. IJRET: International Journal of Research in Engineering and Technology.
8. Tedmori, S., & Al-Najdawi, N. (2014). Image cryptographic algorithm based on the Haar wavelet transform. Information Sciences, 269, 21-34.
9. Navneet, G., & Kaur, A. P. (2014). Review: Analysis and Comparison of Various Techniques of Image Compression for Enhancing the Image Quality. J Basic Appl Eng Res, 1(7), 5-8.
10. Lubis, M. Z., Wulandari, P. D., Pujiyati, S., Hestirianoto, T., Moron, J. R., & Mahdi, D. P. I. (2016). Spectral Analysis Using Haar Wavelet (Original Signal, Denoised Signal, Residual Signal) and Source Level for Whistle Sound of Dolphin (Tursiops aduncus). Journal of FisheriesSciences. com, 10(3), 9.-93.
Copyright (c) 2017 JOURNAL OF APPLIED GEOSPATIAL INFORMATION
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright @2023. This is an open-access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License which permits unrestricted use, distribution, and reproduction in any medium. Copyrights of all materials published in JAGI are freely available without charge to users or / institution. Users are allowed to read, download, copy, distribute, search, or link to full-text articles in this journal without asking by giving appropriate credit, provide a link to the license, and indicate if changes were made. All of the remix, transform, or build upon the material must distribute the contributions under the same license as the original.
