Title: Fuzzy vision image denoising algorithm based on Bayesian estimation

Authors: Cong Ye

Addresses: Department of Computer Science and Technology, Suzhou College of Information Technology, Jiangsu, 215200, China

Abstract: A fuzzy visual image denoising algorithm based on Bayesian estimation is proposed to address the problems of poor denoising performance and long denoising time in traditional image denoising algorithms. First, analyse the noise reduction requirements of fuzzy visual images, construct a visual image degradation model, and obtain fuzzy visual images. Then, analyse the spectrum of the acquired fuzzy visual image, preprocess the spectrum signal by adding a window function, and detect the edge of the fuzzy visual image according to the preprocess result to supplement the fuzzy edge. Based on the supplemented blurred visual image edges, Bayesian estimation is used for image denoising to improve the image denoising effect. The experimental results show that the visual image obtained by the proposed method is relatively clear and has high image quality, indicating that the denoising effect of the algorithm is good and the denoising time is short.

Keywords: Bayesian estimation; blur visual image; spectrum analysis; window function; blur edges.

DOI: 10.1504/IJRIS.2025.145053

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.1, pp.24 - 31

Received: 24 Feb 2023
Accepted: 27 Apr 2023
Published online: 18 Mar 2025 *

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