research-article

Wavelet Denoising of Remote Sensing Image Based on Adaptive Threshold Function

Authors:

Yuqing Ma,

Juan Zhu,

Jipeng HuangAuthors Info & Claims

ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

Pages 256 - 261

https://doi.org/10.1145/3376067.3376087

Published: 25 February 2020 Publication History

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Abstract

Aiming at the problem of edge feature loss caused by conventional threshold function in wavelet transform, a new adaptive threshold function denoising algorithm is proposed based on improved threshold. The algorithm takes advantages of the improved threshold functions, and takes the scale of the current wavelet decomposition as a function adjustment factor, so that the function can be adjusted by adaptive scale transformation, which is more in line with the actual distribution of noise in each scale. A few noisy remote sensing images are tested and the simulation results of MATLAB confirm the merits of the proposed denoising technique compared with other wavelet-based techniques by measuring evaluation metrics such as signal-to-noise ratio and mean square error. Furthermore, the improved threshold function can obtain better visual effects which ensures the detail features in remote sensing images are better preserved.

References

[1]

Patidar P, Gupta M, Srivastava S. Image de-noising by various filters for different noise[J]. International journal of computer applications, 2010, 9(4): 45--50.

Crossref

Google Scholar

[2]

Daubechies I. The wavelet transform, time-frequency localization and signal analysis[J]. IEEE transactions on information theory, 1990, 36(5): 961--1005.

Digital Library

Google Scholar

[3]

Kaur G, Choudhary R, Vats A. A WAVELET APPROACH FOR MEDICAL IMAGE DENOISING[J]. International Journal of Advanced Research in Computer Science, 2017, 8(8).

Crossref

Google Scholar

[4]

Donoho D L, Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage[J]. Journal of the american statistical association, 1995, 90(432): 1200--1224.

Google Scholar

[5]

Donoho D L. De-noising by soft-thresholding[J]. IEEE transactions on information theory, 1995, 41(3): 613--627.

Digital Library

Google Scholar

[6]

Jain P K, Tiwari A K. An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal[J]. Biomedical Signal Processing and Control, 2017, 38: 388--399.

Crossref

Google Scholar

[7]

Liu X L, Liu Z, Li X B, et al. Wavelet threshold de-noising of rock acoustic emission signals subjected to dynamic loads[J]. Journal of Geophysics and Engineering, 2018, 15(4): 1160--1170.

Crossref

Google Scholar

[8]

Wang W B, Dong R Y, Zeng W J, Zhang B, Zheng Y K. A wavelet denoising method for power quality based on an improved threshold and threshold function, Transactions China Electrotech. Soc,2019,34(02):409--418.

Google Scholar

[9]

Chen B, Cui J, Xu Q, et al. Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image[J]. Journal of Central South University, 2019, 26(1): 120--131.

Crossref

Google Scholar

[10]

Agrawal K, Jha A K, Sharma S, et al. Wavelet subband dependent thresholding for denoising of phonocardiographic signals[C]//2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2013: 158--162.

Google Scholar

[11]

Donoho D L, Johnstone I M. Threshold selection for wavelet shrinkage of noisy data[C]//Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE,1994,1:A24-A25 vol. 1.

Google Scholar

[12]

Fan X, Xie W, Jiang W, et al. An improved threshold function method for power quality disturbance signal de-noising based on stationary wavelet transform[J]. Trans. China Electrotech. Soc, 2016, 31: 219--318.

Google Scholar

[13]

Chen Z, Hu Z. Remote sensing image denoising based on improved wavelet threshold algorithm[J]. Bull. Survey. Map, 2018, 4: 28--31.

Google Scholar

[14]

Zhang H J, Zhang D M, Yan W, Chen Z Y, Xin X.Wavelet transform image de-noising algorithm based on improved threshold function, Comput. Appl, 1-6 [2019-10-18]. DOI= https://doi.org/10.19734/j.issn.1001.3695.2018.10.0844.

Crossref

Google Scholar

Cited By

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Goh HLiao LZhang DDai WLim CKurniawan TGoh KCham C(2022)Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy OptimizationEnergies10.3390/en1509308115:9(3081)Online publication date: 22-Apr-2022
https://doi.org/10.3390/en15093081

Index Terms

Wavelet Denoising of Remote Sensing Image Based on Adaptive Threshold Function
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

December 2019

270 pages

ISBN:9781450376822

DOI:10.1145/3376067

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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Shanghai Jiao Tong University: Shanghai Jiao Tong University
Xidian University
TU: Tianjin University

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Association for Computing Machinery

New York, NY, United States

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Published: 25 February 2020

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The Key Program for Science and Technology Development of Jilin Province
The 13th Five-year Plan for Science and Technology Project of the Education Department of Jilin Province
Fundamental Research Funds for the Central Universities
Program for Science and Technology Development of Changchun City

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ICVIP 2019

ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing

December 20 - 23, 2019

Shanghai, China

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Goh HLiao LZhang DDai WLim CKurniawan TGoh KCham C(2022)Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy OptimizationEnergies10.3390/en1509308115:9(3081)Online publication date: 22-Apr-2022
https://doi.org/10.3390/en15093081

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Adaptive wavelet threshold for image denoising by exploiting inter-scale dependency

Combining curvelet transform and wavelet transform for image denoising

A Joint Multiscale Algorithm with Auto-adapted Threshold for Image Denoising

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Adaptive wavelet threshold for image denoising by exploiting inter-scale dependency

Combining curvelet transform and wavelet transform for image denoising

A Joint Multiscale Algorithm with Auto-adapted Threshold for Image Denoising

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