Adaptive denoising method of steel plate surface image based on BM3D

被引：0

作者：

Yang Y. ^{[1
]}

Li Y. ^{[1
]}

Ma Z. ^{[1
]}

Chen F. ^{[1
]}

Huang Q. ^{[1
]}

机构：

[1] Light Alloy Research Institute of Central South University, Changsha

来源：

Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2022年 / 30卷 / 20期

关键词：

adaptive denoising; BM3D algorithms; noise estimation; steel plate surface defect; threshold function;

D O I：

10.37188/OPE.20223020.2510

中图分类号：

学科分类号：

摘要：

An adaptive block-matching and 3D-filtering denoising（BM3D）algorithm based on noise estimation and a threshold function is proposed to solve the problem of the distance threshold selection of the traditional BM3D algorithm not being adaptive and to improve the image quality by removing noise in steel plate images. First，the grid search method is used to obtain different plate defect images under different noise-intensity-based estimations and the final estimate for the best threshold value. Subsequently，the different function fitting effects are compared，and the estimated quadratic curve threshold function and the final estimate of four polynomial threshold functions are determined. Moreover，noise estimation is performed for the new algorithm processing phase. Finally，the new BM3D algorithm is compared with the original BM3D algorithm and other latest denoising algorithms. Experiments show that the algorithm has excellent performance in restoring the edge and detail textures of defective images. Under noise with a standard deviation of 30，the peak signal-to-noise ratio and structural similarity value of the denoising effect of each defective image are above 33 dB and 0. 85，respectively. Moreover，the residual details in the residual image are reduced and are better than those achieved by applying other algorithms. © 2022 Chinese Academy of Sciences. All rights reserved.

引用

页码：2510 / 2522

页数：12

共 21 条

[1]

XUE B, WU Z S., Key technologies of steel plate surface defect detection system based on artificial intelligence machine vision［J］, Wireless Communica⁃ tions and Mobile Computing, 2021, (2021)

[2]

YANG Y, LI D R, HUANG X F., Research on image denoising algorithm based on non-local block matching［J］, International Journal of Information and Communication Technology, 16, 3, (2020)

[3]

SEKHAR B, REDDY P P，, VENKATARAMANA S, Et al., Image denoising using novel social grouping optimization algorithm with transform domain technique［J］, International Journal of Natural Computing Research, 8, 4, pp. 28-40, (2019)

[4]

BUADES A, MOREL J M., A non-local algorithm for image denoising［C］, 2005 IEEE Com⁃ puter Society Conference on Computer Vision and Pattern Recognition, pp. 60-65, (2015)

[5]

HERNANDEZ-GUTIERREZ I V, GALLEGOSFUNES F J, ROSALES-SILVA A J., Improved preclassification non local-means （IPNLM） for filtering of grayscale images degraded with additive white Gaussian noise［J］, EURASIP Journal on Im⁃ age and Video Processing, 2018, 1, pp. 1-14, (2018)

[6]

LU H Q, GE H W., Two-stage non-local means denoising based on hybrid robust weight and improved method noise［J］, Computer Engineering ＆ Sci⁃ ence, 40, 7, pp. 1227-1236, (2018)

[7]

HUO F C, ZHANG W H, WANG Q, Et al., Two-stage image denoising algorithm based on noise localization［J］, Multimedia Tools and Applications, 80, 9, pp. 14101-14122, (2021)

[8]

HUANG X, WOOLSEY G A., Image denoising using Wiener filtering and wavelet thresholding［C］, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Ad⁃ vances in the Fast Changing World of Multimedia （Cat. No. 00TH8532）, pp. 1759-1762, (2000)

[9]

ZHU H, WANG X M., Image denoising by wavelet transform based on new threshold［C］, Big Data Analytics for Cyber-Physical System in Smart City, pp. 208-213, (2021)

[10]

MALLAT S G, ZHANG Z F., Matching pursuits with time-frequency dictionaries［J］, IEEE Trans⁃ actions on Signal Processing, 41, 12, pp. 3397-3415, (1993)

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