关键词: 知识测度, 直觉模糊集, 知识量, 脉冲噪声, 图像去噪

Abstract: A new 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 not only can effectively denoising, but also better protect the characteristics of image edges and non-noise extreme pixels. The comparative experiments are carried out on standard datasets and classical test images, respectively. The experimental results show that the proposed method can accurately identify the image impulse noise and effectively realize image denoising. The overall performance is outperforming 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 for the first time, and excellent evaluation metrics and visual effects are obtained, which creates a new example for the innovative application of this theory in other related fields.

Key words: knowledge measure, intuitionistic fuzzy sets, amount of knowledge, impulse noise, image denoising