An NSCT Image Denoising Method Based on Genetic Algorithm to Optimize the Threshold

被引：1

作者：

Zhang, Zeliang ^{[1
]}

Wang, Haoyang ^{[1
]}

Bi, Xinwen ^{[1
]}

Wu, Jing ^{[2
]}

Cheng, Yanming ^{[3
]}

Lee, Ilkyoo ^{[2
]}

Chen, Jiufei ^{[4
]}

机构：

[1] Beihua Univ, Coll Comp Sci & Technol, Jilin, Peoples R China

[2] Kongju Natl Univ, Div Elect Elect & Control Engn, Gongju Si, Chungcheongnam, South Korea

[3] Beihua Univ, Coll Elect & Informat Engn, Jilin, Peoples R China

[4] Petro China, Oil Refinery Jilin Petrochem Co, Beijing, Peoples R China

来源：

MOBILE INFORMATION SYSTEMS | 2022年 / 2022卷

关键词：

Compendex;

D O I：

10.1155/2022/7847808

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to solve the defect that the threshold value of the NSCT transform method is too large or the real signal coefficients are directly lost during image denoising, an adaptive threshold method of genetic algorithm is used to optimize the NSCT image denoising method. The genetic algorithm is used to generate the initial population, and the genetic operator is determined by selection, crossover, and mutation operations to achieve NSCT threshold optimization. The obtained optimized NSCT threshold is used to process different directions. The coefficients of different scales are processed by using NSCT inverse transform to obtain the denoised image. The results of the case analysis show that the proposed method is used to denoise the image, the peak signal-to-noise ratio of the image after denoising is higher than 30 dB, the image contains rich edge information and detailed information, and the denoising performance is superior.

引用

页数：7

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