模糊知识测度下图像脉冲噪声去除方法OACSCDCSTPCD

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.