光学4f系统灰度误差补偿的实现

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文章编号:10019081(2013)07197303

doi:10.11772/j.issn.10019081.2013.07.1973

摘 要:

为补偿光学4f系统灰度误差，提出基于直方图匹配和径向基函数(RBF)神经网络的灰度误差补偿方法。首先利用径向基函数神经网络拟合经光学4f系统输出图像的直方图与对应输入图像的直方图之间的非线性变换，得到输出图像与输入图像的直方图匹配变换曲线的最优估计；再依据直方图匹配曲线的最优估计对经光学4f系统的输出图像进行直方图匹配，得到灰度误差补偿后的图像。利用实际的光学4f系统进行光学实验，灰度误差补偿后图像的信噪比平均提高了2.96dB，视觉效果明显改善。实验结果表明，该方法能有效补偿光学4f系统灰度误差，提高基于光学4f系统的光学信息处理的精度。

关键词:光学4f系统；灰度误差；径向基函数神经网络；直方图匹配

中图分类号: TN911.74文献标志码:A

英文标题

Implementation of gray level error conpensation for optical 4f system

英文作者名

HAN Liang*, JIANG Ziqi, PU Xiujuan

英文地址(

College of Communication Engineering, Chongqing University, Chongqing 400030, China英文摘要)

Abstract:

To compensate the gray level error in optical 4f system, a method for gray level error compensation based on histogram matching and Radial Basis Function (RBF) neural network was proposed. The nonlinear transformation of histogram between input and output images in optical 4fSystem was fitted by RBF neural network, then the optimal estimation of curve for histogram matching between input and output images was obtained. The gray level error compensation image was obtained by utilizing histogram matching according to the optimal estimation of curve for histogram matching. The average Peak SignaltoNoise Ratio (PSNR) gain achieved was 2.96dB and the visual effect of images processed was improved by utilizing the proposed method in actual optical 4f system. The experimental results show the gray level error in optical 4f system can be compensated effectively and the precision of optical information processing was improved by the proposed method.

To compensate the gray level error in optical 4f system, one method for gray level error compensation based on histogram matching and Radial Basis Function (RBF) neural network is proposed. The nonlinear transformation of histogram between input and output images in optical 4f System is fitted by RBF neural network, then the optimal estimation of curve for histogram matching between input and output images is obtained. The gray level error compensation image is obtained utilizing histogram matching according to the optimal estimation of curve for histogram matching. The averagely PSNR gain achieved is 2.96 dB and the visual effect of images processed is improved utilizing the proposed method in actual optical 4f system. The experimental results show the gray level error in optical 4f system can be compensated effectively and the precision on optical information processing is improved by the proposed method.

英文关键词Key words: optical 4f system; gray level error; Radial Basis Function (RBF) neural network; histogram matching

0 引言

光学信息处理是基于光学频谱分析，利用傅里叶综合技术，通过空域或频域调制，借助空间滤波技术对光学信息进行处理的过程。光学信息处理系统的最大优势是其高速度、并行性及互连性［1］，光学4f系统是光学信息处理的重要实现系统之一。光学4f系统已经被应用于图像光学小波变换［2］、数字全息［3］及工件误差检测［4］等领域，显示出良好的应用前景。

由于光学器件和实验环境的原因，光学4f系统存在一定的噪声干扰，主要包括随机噪声、相干噪声以及系统灰度误差等。为此，Migukin等［5］提出一种利用背景补偿和相位调整的光学4f系统相位恢复方法，有效抑制随机噪声和相干噪声；Xu等［6］提出一种利用多谱点图像融合的光学4f系统降噪方法，既能有效去除随机噪声和低频相干噪声，又能较好地保存图像的有用信息；Katkovnik等［7］提出一种利用空间光调制器(Spatial Light Modulator, SLM)进行相位调制的光学4f系统降噪方法，有效抑制光学4f系统中的衍射产生的噪声；徐鑫等［8-9］提出利用阶跃响应的4f光学系统图像复原方法和应用图像纹理连续性的非下采样轮廓波变换域降噪方法，有效处理系统中的随机噪声和相干噪声，同时注重保护图像细节；李灿等［10］提出基于液晶纯相位光调制器的4f 系统去噪方法，主要抑制透镜等光学器件引入的随机噪声。但是，针对光学4f系统灰度误差的补偿方法还没有出现，这将对基于光学4f系统的光学信息处理的精度产生一定影响。