BP神经网络用于大视场显示设备的畸变校正

Application of BP Neural Network in Distortion Correction of Large FOV Display

大视场光电成像显示设备中会出现光学系统引起的图像几何畸变现象。为了提高显示设备畸变校正效果,并克服传统BP算法存在局部极小点、收敛速度慢等缺点,采用了基于优化理论的LM算法来改进传统BP神经网络算法。提出一种含有双层隐含层的BP神经网络畸变校正方法,可在不确知畸变数学模型情况下,实现自适应地建立畸变图像与原始图像之间的高精度映射关系。在Matlab平台上进行算法的深入分析和比较。仿真结果表明,双隐含层BP神经网络算法易于实现、数据处理能力强、校正精度高。与多项式拟合方法的畸变校正模型相比,基于双隐含层BP神经网络算法的畸变校正模型的各项精度指标提升显著。

Geometric distortion may appear in large Field-of-View （FOV） electro-optic image display equipment, which is caused by the optical system. To improve image distortion correction effect and overcome the limitations of the traditional BP algorithm, such as local minimum and slow convergence speed, Levenberg-Marquardt algorithm based on optimizing theory was used. Then the distortion correction method based on BP neural network containing two hidden layers was proposed, which could achieve high precision of mapping between distortion image and original image self-adaptively without knowing the mathematic model. The algorithms were analyzed and compared in depth in the Matlab platform. The simulation result shows that the BP neural network algorithm with double hidden layers can be realized easily, achieve high precision, and has good data processing ability. Compared with the distortion correction model based on polynomial fitting method, all the precision indexes of the distortion correction model based on BP neural network with double hidden layers are improved observably.