摘要
利用不完全Beta变换对红外图像进行自适应的增强处理。变换的参数利用混沌优化算法给出。为了提高优化迭代的计算速度,运用BP神经网络对不完全Beta变换进行函数逼近。仿真结果表明,该算法能自适应地进行图像增强处理。增强后的图像灰度分布更均匀,对比度得到明显提高。
The incomplete beta transformation is used to process the infrared images enhancement.The parameters of the transformation are provided by chaotic optimization method.To improve the optimization iterative epoch,a BP neural net-work are trained to approximate the incomplete beta transformation function.The simulation results show that the above algorithm is able to adaptively enhance the images grey.The enhanced images have more equal grey distribution.So the contrast is rapidly progressed.
出处
《计算机工程与应用》
CSCD
北大核心
2003年第12期4-6,共3页
Computer Engineering and Applications
基金
国家自然科学基金资助项目(编号:69674005)
关键词
图像增强
混沌优化
神经网络
不完全Beta变换
Image enhancement ,Chaotic optimization,Neural network,Incomplete beta transformation
