摘要
提出了一种基于径向基函数(RBF)神经网络的闪光照相网栅图像修补算法,该方法采用滑动窗口方法将待修补的网栅图像分为若干子块,然后在每个子图像内分别引入RBF神经网络,将栅孔内图像作为已知数据计算RBF网络参数,并以此对每个子图像进行修补,数值试验表明,该算法能较好地再现图像边缘信息,修复的图像在信噪比和视觉方面都优于线性插值和样条插值的结果。
To solve the problem of flash radiographic anti-scatter grid image inpainting, a radial basis function （RBF） neural network based image inpainting algorithm is proposed. First the anti-scatter grid image is divided into a series of blocked images. Then the weights of the RBF network are estimated and a continuous function is constructed in each blocked image, and with them the pixels of missing information can be {illed in. The experimental results show that the new algorithm has better general performance in inpainting quality and boundary maintenance compared with the linear interpolation and spline interpolation meth- od.
出处
《强激光与粒子束》
EI
CAS
CSCD
北大核心
2013年第3期751-754,共4页
High Power Laser and Particle Beams
基金
中国工程物理研究院科学技术发展基金项目(2009A0203013
2010B0202021)
关键词
图像修补
闪光照相
神经网络
径向基函数
image inpainting
flash X-ray radiography
neural network
radial basis function
