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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.

The ordered weighted averaging (OWA) operator provides a unified framework for multiattribute decision making (MADM) under uncertainty. In this paper, we attempt to tackle some issues arising from the quantifier guided aggregation using OWA operators. This allows us to consider a more general case involving the generation of quantifier targeted at the specified decision maker (DM) by using sample information. In order to do that, we first develop a repeatable interactive procedure in which with the given sample values, and the expected values the DM involved provides with personal preferences, we build nonlinear optimal models to extract from the DM information about his/her decision attitude in an OWA weighting vector form. After that, with the obtained attitudinal weighting vectors we suggest a suitable quantifier just for this DM by means of the piecewise linear interpolations. This obtained quantifier is totally derived from the behavior of the DM involved and thus inherently characterized by his/her own attitudinal character. Owing to the nature of this type of quantifier, we call it the subjective expected value of sample information-induced quantifier. We show some properties of the developed quantifier. We also prove the consistency of OWA aggregation guided by this type of quantifier. In contrast with parameterized quantifiers, our developed quantifiers are oriented toward the specified DMs with proper consideration of their decision attitudes or behavior characteristics, thus bringing about more intuitively appealing and convincing results in the quantifier guided OWA aggregation. [ABSTRACT FROM PUBLISHER]