电子学报2023，Vol.51Issue(11)：3248-3261,14.DOI:10.12263/DZXB.20220622
模糊知识测度下图像脉冲噪声去除方法
Removal of Impulse Noise in Grayscale Images Under Fuzzy Knowledge Measure
摘要
Abstract
Aiming at the problems of inaccurate noise detection and unsatisfactory filtering effect in image impulse noise removal methods,this paper introduces a new theory of knowledge measurement,and proposes a new method of image denoising based on fuzzy knowledge.Firstly,the fuzzy knowledge measurement axiom system is improved and simplified,and a new fuzzy knowledge measurement parameterized model is proposed based on this.Secondly,the proposed model is used to calculate and analyze the similarity characteristics and the differences in the amount of knowledge between extreme points and neighboring pixels in the noise image,and effectively identify and distinguish impulse noise from ordinary extreme pixels.Finally,a maximum average correlation knowledge filter algorithm is proposed to achieve image denoising.The com-parison experiments are carried out on the standard data set and the test image respectively,and the experimental results show that the proposed method can accurately identify the impulse noise,effectively improve the image filtering quality,and the main indicators and performance of the denoising results are significantly better than other similar algorithms.Compared with the average value of the comparison method,the index PSNR has an average increase of 7.8%,and the SSIM has an aver-age increase of 10%.This paper applies the new theory of knowledge measurement to image denoising for the first time and achieves excellent results,which creates a new example for the innovative application of this theory in other related fields.关键词
知识测度/模糊集/图像滤波/脉冲噪声检测/自适应窗口Key words
knowledge measure/fuzzy set/image filtering/impulse noise detection/adaptive window分类
信息技术与安全科学引用本文复制引用
郭凯红,崔明茜,刘婷婷..模糊知识测度下图像脉冲噪声去除方法[J].电子学报,2023,51(11):3248-3261,14.基金项目
国家自然科学基金(No.71771110) （No.71771110）
辽宁省教育厅基本科研项目(No.LJKQZ2021023) （No.LJKQZ2021023）
辽宁省博士科研启动基金计划项目(No.2020-BS-082)National Natural Science Foundation of China(No.71771110) （No.2020-BS-082）
Basic Research Project of the Education Department of Liaoning Province(No.LJKQZ2021023) （No.LJKQZ2021023）
Ph.D.Research Startup Foundation of Liaoning Prov-ince(No.2020-BS-082) （No.2020-BS-082）
