Image restoration using a neural network
Image restoration using a neural network
复制标题
DOI：
10.1109/29.1641
复制
发表时间：
1988-07
期刊：
影响因子：
--
通讯作者：
Yi-Tong Zhou;R. Chellappa;A. Vaid;B. K. Jenkins
中科院分区：
文献类型：
--
作者：
Yi-Tong Zhou;R. Chellappa;A. Vaid;B. K. Jenkins
提出了一种基于神经网络的灰度图像恢复方法，该方法用于恢复由已知的平移不变模糊函数和加性噪声引起的灰度图像。一个神经网络模型被用来表示一个可能的非平稳图像，其灰度函数是神经元状态变量的简单和。恢复过程包括两个阶段：神经网络模型的参数估计和图像重建。由于该模型的容错性和计算能力，高质量的图像是使用这种方法。文中还给出了一种计算复杂度较低的实用算法。从原始和退化图像的原型学习模糊参数的过程概述。>
An approach for restoration of gray level images degraded by a known shift invariant blur function and additive noise is presented using a neural computational network. A neural network model is used to represent a possibly nonstationary image whose gray level function is the simple sum of the neuron state variables. The restoration procedure consists of two stages: estimation of the parameters of the neural network model and reconstruction of images. Owing to the model's fault-tolerant nature and computation capability, a high-quality image is obtained using this approach. A practical algorithm with reduced computational complexity is also presented. A procedure for learning the blur parameters from prototypes of original and degraded images is outlined. >