针对闪光照相系统模糊较大、成像信噪比较低的问题，提出了一种基于BP神经网络的闪光照相图像复原方法。该方法利用BP神经网络的泛化能力，用样本图像对网络进行训练，建立退化图像与真实图像之间的非线性映射关系，然后将待复原图像分区，利用训练好的BP神经网络对待复原图像的边界区域进行复原处理。数值试验表明，在系统点扩展函数未知的情况下，该算法能较好再现图像边缘信息，复原出的图像在信噪比和视觉方面都有较大提高。

A BP neural network based image restoration algorithm is proposed against the blur of flash radiographic images with low signal-to-noise ratios. Firstly the degraded image and the sample image are both divided into the edge part and the smooth part. Secondly the nonlinear mapping relationship between the degraded image and its original image is established by training the BP neural network which has the ability of learning, remembrance and generalizing with the edge part of the sample image. Finally the edge part of the degraded image could be restored by the trained neural network. Numerical results show that this method could restore the image information near edges when the systematic point-spread function is unknown, and the restored image is greatly improved in the signal-to-noise ratio and in the visual.