Image Compression and Regeneration by Nonlinear Associative Silicon Retina

Mamoru TANAKA  Yoshinori NAKAMURA  Munemitsu IKEGAMI  Kikufumi KANDA  Taizou HATTORI  Yasutami CHIGUSA  Hikaru MIZUTANI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E75-A   No.5   pp.586-594
Publication Date: 1992/05/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Dynamics--Adaptive, Learning and Neural Systems--)
Category: Neural Systems
Keyword: 
cellular neural network,  retina,  image compression,  image regeneration,  dynamic quantization,  error correcting,  regularization,  

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Summary: 
Threre are two types of nonlinear associative silicon retinas. One is a sparse Hopfield type neural network which is called a H-type retina and the other is its dual network which is called a DH-type retina. The input information sequences of H-type and HD-type retinas are given by nodes and links as voltages and currents respectively. The error correcting capacity (minimum basin of attraction) of H-type and DH-type retinas is decided by the minimum numbers of links of cutset and loop respectively. The operation principle of the regeneration is based on the voltage or current distribution of the neural field. The most important nonlinear operation in the retinas is a dynamic quantization to decide the binary value of each neuron output from the neighbor value. Also, the edge is emphasized by a line-process. The rates of compression of H-type and DH-type retinas used in the simulation are 1/8 and (2/3) (1/8) respectively, where 2/3 and 1/8 mean rates of the structural and binarizational compression respectively. We could have interesting and significant simulation results enough to make a chip.