基于多维联想记忆神经网络的图像回忆

多维联想记忆神经网络在高噪声情况下图像回忆效果差。针对该问题，将图像矩阵垂直分成4个同型小矩阵，依次将4个小矩阵垂向聚合成一个新矩阵，以新矩阵的列向量作为库向量。数值实验结果表明，相比2个列向量构成的库向量，以4个列向量构成的库向量进行回忆的灰度图像更清晰且效率更高。

Image Recalling Based on Multidimensional Associative Memory Neural Network

Multidimensional associative memory neural networks can be used for image recalling.When the image is corrupted by high noise,the recalling image is not clear using the traditional method.In order to make the recalling image clearer,one library vector made up of four column vectors is used in the recalling image to take the place of the other traditional library vectors made up of two column vectors.That is,a new matrix is formed by vertically dividing the mage matrix into four small matrices of the same order and vertically concatenating the four matrices in order.A column vector of the new matrix is regarded as a library vector.Numerical examples show that the restored image is clearer and the recalling process spends less time when the former library vector is used.