Costantini Giovanni, Casali Daniele, Perfetti Renzo
IEEE Trans Neural Netw. 2006 Mar;17(2):519-22. doi: 10.1109/TNN.2005.863465.
A design procedure is presented for neural associative memories storing gray-scale images. It is an evolution of a previous work based on the decomposition of the image with 2L gray levels into L binary patterns, stored in L uncoupled neural networks. In this letter, an L-layer neural network is proposed with both intralayer and interlayer connections. The connections between different layers introduce interactions among all the neurons, increasing the recall performance with respect to the uncoupled case. In particular, the proposed network can store images with the commonly used number of 256 gray levels instead of 16, as in the previous approach.
本文提出了一种用于存储灰度图像的神经联想存储器的设计方法。它是基于先前工作的改进，之前的工作是将具有2L个灰度级的图像分解为L个二进制模式，并存储在L个非耦合神经网络中。在本文中，提出了一种具有层内和层间连接的L层神经网络。不同层之间的连接引入了所有神经元之间的相互作用，相对于非耦合情况提高了召回性能。特别是，所提出的网络可以存储具有常用的256个灰度级的图像，而不像先前方法那样只能存储16个灰度级的图像。
