Method of Paper's Disease Denoising Base on Adaptive with Threshold Wavelet Transform

Ya Juan Shi; Feng Tao Li

doi:10.4028/www.scientific.net/AMM.731.214

Paper Titles

Image Tone Transformation Based on Block Segmentation and the Statistical Characteristics of Gray-Levels
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Extracting Golden Area from Image Based on Canny Operator
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Research on the Algorithm of Defect Feature Extraction in Trademark Printing
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Application of Object-Based Image Processing Technique in Printing Defect Online Detection
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Method of Paper's Disease Denoising Base on Adaptive with Threshold Wavelet Transform
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Hybrid Screening Technology by Error Diffusion Algorithm
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Research on the Evaluation of Digital Prints Quality Based on Noise
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A Study of Semantic Query Expansion Based on Domain Ontology and Local Co-Occurrence Probability Model
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Research on Chinese Natural Language Query Interface to Database Based on Syntax and Semantic
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 731Method of Paper's Disease Denoising Base on...

Method of Paper's Disease Denoising Base on Adaptive with Threshold Wavelet Transform

Abstract:

This paper proposed an improved method based on adaptive with threshold wavelet transform denoising, according to image is often affected by noise pollution in the process of acquisition and transmission, compared with the advantages and disadvantages of traditional digital filtering method. It extracts the structure information and details of the image. It can adaptively select wavelet transform of optimal decomposition level and soft threshold to achieve the optimal noise reduction effect. The Simulation results demonstrate that this theory can effectively filter Gaussian noise and Salt and pepper noise, at the same time well protect the image details and achieve better visual effects.

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Periodical:

Applied Mechanics and Materials (Volume 731)

Pages:

214-217

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.731.214

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Online since:

January 2015

Authors:

Ya Juan Shi, Feng Tao Li

Keywords:

Adaptive, Digital Filtering, PSNR, Threshold, Wavelet Transform (WT)

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