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.

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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

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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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