The image auto-focusing method based on artificial neural networks

Chen Guojin, Ling Yongning, Zhu Miao-fen, Wang Wan-qiang
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引用次数: 3

Abstract

According to the image feature extraction capacity based on wavelet transformation and the nonlinear, self-adaptive and pattern recognition capacity based on artificial neural networks, the image auto-focusing method based on artificial neural networks is put forward. The wavelet components' statistics obtained by the wavelet transform are taken as the inputs of the 5 layer BP neural network model. The model identifies the image definition applying the steepest descent method of the additional momentum in a variable step to adjust the network weights. The model is first trained by 75 images from a training set, and then is tested by 102 images from a testing set. The results show that it is a very effective identification method which can obtain a higher recognition rate.
基于人工神经网络的图像自动调焦方法
根据基于小波变换的图像特征提取能力和基于人工神经网络的非线性、自适应和模式识别能力,提出了基于人工神经网络的图像自动聚焦方法。将小波变换得到的小波分量统计量作为5层BP神经网络模型的输入。该模型采用变步长附加动量的最陡下降法来调整网络权值,从而识别图像的清晰度。首先用一个训练集中的75张图像对模型进行训练,然后用一个测试集中的102张图像对模型进行测试。结果表明,该方法可以获得较高的识别率,是一种非常有效的识别方法。
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