Improving Wavelet Threshold De-noising Applied on Parts Detection

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ID: 85445
2018
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Ranked #263 of 300 articles by views in matec web of conferences

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Abstract
The traditional filtering methods such as median filter and mean filter always blurrs image features, resulting in poor noise reduction effect. Wavelet transform has unique adaptability due to its variable resolution, which can better implement wavelet denoising on the basis of image feature. Aiming at the shortcoming of traditional wavelet transform threshold denoising, based on the hard threshold and soft threshold function, this paper proposes improved adaptive thresholding function. By comparing and validating, this method obtains the smaller mean square error (MSE) and higher peak signal to noise ratio. Meanwhile, this method improves the quality of detection images, and reduces the impact on images brought by noise from external enviroment and internal system. So, this can be applied to image noise reduction of the detection system.
Reference Key
lizhou2018improvingmatec Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lizhou, Cheng;Jian, Mao;Hongyuan, Wei;
Journal matec web of conferences
Year 2018
DOI
DOI not found
URL
https://doi.org/10.1051/matecconf/201823204084
Keywords
Chemical technology Engineering (General). Civil engineering (General) Medical technology computer engineering. computer hardware electronic computers. computer science

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