{"title":"Mathematical Image Analysis of Light Fused with Computer Fuzzy Algorithm","authors":"Jia-Ci Tang","doi":"10.1109/ICCS56273.2022.9987735","DOIUrl":null,"url":null,"abstract":"The fuzzy mathematics theory was born in 1965, and it takes uncertain things as the research object. In recent years, the research on fuzzy mathematics theory has been deepened again and again. Therefore, the theoretical research of fuzzy mathematics is widely used in aerospace, petrochemical, nuclear power and many other industries. At the same time, under the impetus of advanced computer science and technology, fuzzy mathematics theory is the most important in the field of computer application. Digital image processing technology is to use the computer's recognition and operation functions to carry out image processing, and fuzzy mathematical theory is also used in the process of image processing. In order to improve the accuracy and precision of image processing, it is a problem to be solved urgently to enhance digital images by using fuzzy operations. Under this background, this paper uses the quadratic element function to process the data, and realizes the mathematical analysis of the image under the complex background. However, conventional digital images cannot be used effectively for mathematical computer modeling. In the application process of optical recognition technology, how to balance the visible light image under various constraints is the key issue of applying computer fuzzy method to light analysis. For images with complex backgrounds, this paper adopts the flow mathematics method of different adjacent regions to solve traditional flow problems under complex backgrounds. On this basis, the VNM (Von Neumann and Morgenstern) function is used to reduce the noise of the image in the complex background, and the computer blur algorithm is used to maximize the light intensity and spatial distribution of the digital image. Finally, pixel-level fusion of digital images is realized.","PeriodicalId":382726,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Systems (ICCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 2nd International Conference on Computer Systems (ICCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCS56273.2022.9987735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The fuzzy mathematics theory was born in 1965, and it takes uncertain things as the research object. In recent years, the research on fuzzy mathematics theory has been deepened again and again. Therefore, the theoretical research of fuzzy mathematics is widely used in aerospace, petrochemical, nuclear power and many other industries. At the same time, under the impetus of advanced computer science and technology, fuzzy mathematics theory is the most important in the field of computer application. Digital image processing technology is to use the computer's recognition and operation functions to carry out image processing, and fuzzy mathematical theory is also used in the process of image processing. In order to improve the accuracy and precision of image processing, it is a problem to be solved urgently to enhance digital images by using fuzzy operations. Under this background, this paper uses the quadratic element function to process the data, and realizes the mathematical analysis of the image under the complex background. However, conventional digital images cannot be used effectively for mathematical computer modeling. In the application process of optical recognition technology, how to balance the visible light image under various constraints is the key issue of applying computer fuzzy method to light analysis. For images with complex backgrounds, this paper adopts the flow mathematics method of different adjacent regions to solve traditional flow problems under complex backgrounds. On this basis, the VNM (Von Neumann and Morgenstern) function is used to reduce the noise of the image in the complex background, and the computer blur algorithm is used to maximize the light intensity and spatial distribution of the digital image. Finally, pixel-level fusion of digital images is realized.
模糊数学理论诞生于1965年,它以不确定的事物为研究对象。近年来,模糊数学理论的研究不断深入。因此,模糊数学的理论研究被广泛应用于航空航天、石油化工、核电等诸多行业。同时,在先进的计算机科学技术的推动下,模糊数学理论在计算机应用领域中占有重要地位。数字图像处理技术是利用计算机的识别和运算功能进行图像处理,在图像处理过程中也运用了模糊数学理论。为了提高图像处理的精度和精度,利用模糊运算对数字图像进行增强是一个亟待解决的问题。在此背景下,本文采用二次元函数对数据进行处理,实现了复杂背景下图像的数学分析。然而,传统的数字图像不能有效地用于数学计算机建模。在光学识别技术的应用过程中,如何在各种约束条件下平衡可见光图像是将计算机模糊方法应用于光分析的关键问题。对于具有复杂背景的图像,本文采用不同相邻区域的流动数学方法来解决传统的复杂背景下的流动问题。在此基础上,利用VNM (Von Neumann and Morgenstern)函数降低图像在复杂背景下的噪声,利用计算机模糊算法最大化数字图像的光强和空间分布。最后,实现了数字图像的像素级融合。