Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

Abstract

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

Correlation transmissibility damage indicator for deterioration performance analysis of hydropower generator unit

“…where C r denotes the relative difference between the comparison sequence x i (k) and the reference sequence y 0 at time point k, also known as the correlation coefficient of x i to y 0 at the moment k. ζ is the resolution factor used to reduce the influence of extreme values on the calculation and takes the value of ζ 2(0,1). It is taken ζ ≤ 0.5 in practical applications and ζ = 0.5 (typically) (Tong et al (2020)) in this study.…”

“…ζ is the resolution factor used to reduce the influence of extreme values on the calculation and takes the value of ζ ∈(0,1). It is taken ζ ≤ 0.5 in practical applications and ζ = 0.5 (typically) (Tong et al (2020)) in this study.…”

Oral CT Image Processing Based on Oral CT Image Filtering Algorithm

“…The three-dimensional view of the human body is constructed by scanning the parts of the human body with X-rays. The grayscale of CT imaging will reflect the absorption intensity of X-rays by various organs of the patient, thereby helping medical workers accurately judge the condition [ 6 – 8 ]. Due to the imaging principle of CT image and the complex environmental factors of collecting the data of patients, it will lead to the problem of uneven grayscale or grayscale difference in CT imaging, which cannot help medical workers more accurately judge the symptoms of patients.…”

Line segment detection algorithm in image extraction improvement study

“…With the continuous progress of science and technology and the promotion of intelligent devices, the quality of captured images is getting higher and higher. However, the demand for massive image processing has also increased, including the processing of digital document images such as personal ID photos and reports [1]. For these massive image information, manual processing is not only inefficient, but also easy to recognize errors, resulting in economic losses [2].…”