基于脉冲耦合神经网络和图像熵的各向异性扩散模型研究∗
Study of anisotropic diffusion mo del based on pulse coupled neural network and image entropy
摘要
Abstract
In image processing, most of the anisotropic diffusion models based on partial differential equation use gradient information to detect image edge. If the image edge is seriously polluted by noise, these methods would not be able to detect image edge, so the edge features cannot be retained. Pulse coupled neural network (PCNN) has the property that similar input neurons can generate pulse at the same time; this property is used to process the noisy image, and we can get an image entropy sequence. The image entropy sequence which will be used as an edge detecting operator is introduced into the diffusion equation, and this will not only reduce the defects produced when the gradient is used as an edge detecting operator so it is easily affected by the noise, but the area image information can also retain more completely. Then, we will use the rule of minimum cross entropy to search for a minimum threshold, which would satisfy the condition that the information difference between noisy image and denoised image is the minimum. The optimal threshold designed will control diffusion intensity reasonably, and the anisotropic diffusion model based on pulse coupled neural network and image entropy (PCNN-IEAD) can be established. Analysis and simulation results show that the proposed model preserves more image information than the classical ones. It removes the image noise and at the same time protects the edge texture details of the image; the proposed model retains the area image information more completely, the performance indexes can also confirm the superiority of the new model. In addition, the operating time of the proposed model is shorter than that of the classical models, therefore, the proposed model may be the ideal one.关键词
图像去噪/脉冲耦合神经网络/图像熵/最小交叉熵Key words
image denoising/pulse coupled neural network/image entropy/minimum cross entropy引用本文复制引用
郭业才,周林锋..基于脉冲耦合神经网络和图像熵的各向异性扩散模型研究∗[J].物理学报,2015,(19):1-11,11.基金项目
国家自然科学基金(批准号:11202106,61201444)、教育部高等学校博士学科点专项科研基金(批准号:20123228120005)、江苏省“信息与通信工程”优势学科建设项目、江苏省气象探测与信息处理重点实验室开放课题(批准号：KDXS1204, KDXS1403)、江苏省青蓝工程和江苏省高校自然科学研究项目(批准号:13KJB170016)资助的课题.@@@@* Project supported by the National Natural Science Foundation of China (Grant Nos.11202106,61201444), the Spe-cialized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20123228120005), the Jiangsu Information and Communication Engineering Preponderant Discipline Platform, China, the Jiangsu Key Laboratory of Me-teorological Observation and Information Processing (Grant Nos. KDXS1204, KDXS1403), the Jiangsu Qing Lan Project, and the Natural Sciences Fundation from the Universities of Jiangsu Province of China (Grant No.13KJB170016) （批准号:11202106,61201444）
