现代电影技术Issue(12)：56-61,6.DOI:10.3969/j.issn.1673-3215.2024.12.008
深度反卷积神经网络优化下的低质图像去模糊数学模型
Deblurring mathematical model of low quality image under deep deconvolution neural network optimization
摘要
Abstract
Due to the long storage time,negative degradation,storage space physical environment and other reasons,ag-ing,fading,scratches and blur and other problems would come across to motion picture film as time goes by.Followed with the conversion to digital format for storage,most of classic data motion picture film would appear to have random noise,low-quality image or other problems.In order to ease off problems listed above,this paper proposed a mathematical model of defuzzification of low quality images,which designed based on deep deconvolution neural network optimisation.The degradation equation of low quality images is primarily established by using Poisson distribution method to analyze the random noise of low quality images.Then the initial frame of the address image deblurring mathematical model is con-structed by using the deep deconvolution neural network to optimize it.The method should confirm the loss function,and complete the construction of the low quality image deblurring mathematical model.The experimental results show that the mathematical model presented in this paper presents good deblurring effect for low quality images in practical application,and the peak signal-to-noise ratio is high.It can be applied in the restoration of classic data motion picture,and has a good application prospect in the field of deblurring low-quality images.关键词
图像去模糊/低质图像/深度反卷积神经网络/模糊图像/图像处理Key words
Image Deblurring/Low Quality Image/Deep Deconvolution Neural Network/Blurred Image/Image Processing分类
信息技术与安全科学引用本文复制引用
亓金锋..深度反卷积神经网络优化下的低质图像去模糊数学模型[J].现代电影技术,2024,(12):56-61,6.基金项目
山东省2024年度艺术科学重点课题"全面乡村振兴视域下山东省农村公共文化服务体系建设路径优化研究"(L2024Z05100824). （L2024Z05100824）
