基于改进各向异性扩散的图像去噪算法  

An improved anisotropic diffusion algorithm for the research of image denoising

作　　者：张心如 周先春[3] 汪志飞 王文艳 杨传兵 Zhang Xinru;Zhou Xianchun;Wang Zhifei;Wang Wenyan;Yang Chuanbing(School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044 [2]南京信息工程大学江苏省大气环境与装备技术协同创作中心,南京210044 [3]南京信息工程大学人工智能学院,南京210044

出　　处：《电子测量技术》2022年第17期113-119,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(11202106,61302188);江苏省“信息与通信工程”优势学科建设项目;江苏高校品牌专业建设工程资助项目;国家级大学生创新创业训练计划项目(202110300057)资助。

摘　　要：针对纹理等细节信息丢失和图像边缘退化的问题,本文提出了一种基于l_(2)范数的改进各向异性扩散模型。本文首先将PM模型和LCC模型相结合,根据图像梯度的变化,构建局部图像梯度模值与扩散强度之间的关系,不同的梯度模值选择不同的扩散函数;然后利用l_(2)范数确定扩散函数中的梯度阈值,进一步提高去噪模型的泛化能力。实验结果表明,该模型不仅可以解决传统PM模型存在的孤立点问题,而且能够有效地保护图像边缘特征和轮廓结构的完整性,与原始算法相比图像信噪比提升了1.47~1.57 dB,结构相似度提高了17%,在保证去噪效果的同时提高了去噪效率。In order to solve the problem of texture loss and image edge degradation,an improved anisotropic diffusion model is proposed in this paper.Firstly,the PM model and LCC model are combined.According to the changes of image gradient,the relationship between the gradient modulus of local image and the diffusion intensity is constructed.Different gradient modulus values are selected for different diffusion functions.Then,the l_(2)-norm is used to determine the gradient threshold in the diffusion function,which further improves the generalization ability of the proposed model.Experimental results show that this model can not only solve the problem of outliers existing in the traditional PM model,but also effectively protect the integrity of image edge features and contour structure.Compared with the original algorithm,the image signal-to-noise ratio is improved by 1.47~1.57 dB,and the structural similarity is improved by 17%,the denoising efficiency is improved while ensuring the denoising effect.

关 键 词：各向异性扩散 PM模型 扩散函数 l_(2)范数 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置] TN911.73[自动化与计算机技术—控制科学与工程]