基于直觉模糊知识量的图像噪声检测与去除

Detection and Removal of Noise in Images Based on Amount of Knowledge Associated with Intuitionistic Fuzzy Sets

针对现有依赖于有缺陷的直觉模糊熵(IFE)理论的图像噪声检测算法的不足,引入最新知识测度(KM)理论及模型,提出一种基于直觉模糊知识量(IFAK)的图像噪声检测与去除方法。噪声检测阶段,基于直觉模糊最大知识量确定噪声图前景、背景最佳平均灰度值,据此构建噪声检测参数化模型,实现噪点及疑似噪点的概率标记,表现出优良的噪声检测能力。噪声去除阶段,利用噪声概率矩阵提出一种基于直觉模糊知识量及概率噪声的去噪模型,在有效去噪的同时,更好地保护图像边缘及非噪声极值像素的特征。对比实验针对标准数据集及经典测试图分别进行,实验结果表明,所提方法能够准确识别图像脉冲噪声,有效实现图像去噪,整体性能及表现优于同类其他算法,关键指标值PSNR提升14.81%,SSIM提升11.35%。将知识测度新理论应用于图像去噪中,取得优良的评价指标与视觉效果,同时也实现该理论在其他相关领域的创新应用。

In response to the shortcomings of existing image noise detection algorithms that rely on the flawed intuitionistic fuzzy entropy(IFE)theory,a method of image noise detection and removal based on intuitionistic fuzzy amount of knowledge(IFAK)is proposed by introducing the latest knowledge measure(KM)theory and model.In the noise detection stage,the optimal average intensity of the noisy image foreground and background is determined based on the maximum IFAK,and the parametric model of noise detection is constructed accordingly to mark the probability of noise pixels and suspected noise pixels,showing excellent performance of noise detection.In the noise removal stage,a denoising model based on IFAK and probability of noise pixels is proposed by using the noise probability matrix,which can not only effectively denoise,but also better protect the characteristics of image edges and non-noise extreme pixels.Comparative experiments are carried out on standard datasets and classical test images,respectively.Experimental results show that the proposed method can accurately identify the image impulse noise and effectively realize image denoising.The overall performance outperforms other similar algorithms.The key metrics PSNR and SSIM are increased by 14.81%and 11.35%,respectively.In this paper,the latest KM theory is applied to image denoising,and excellent evaluation metrics and visual effects are obtained,while innovative applications of this theory in other related fields are also achieved.