Title :
Duality theory of cellular neural networks for image compression and regeneration
Author :
Tanaka, Mamoru ; Nakamura, Yoshinori ; Ikegami, Munemitsu ; Chigusa, Yasutami ; Mizutani, Hikaru
Author_Institution :
Sophia Univ., Tokyo, Japan
Abstract :
Image compression and regeneration by nonlinear associative cellular neural networks (CNNs) that can be used as retina chips are addressed. One is a sparse Hopfield-type neural network that is called an H-type CNN and the other is its dual network and is called a DH-type CNN. Their input information sequences are given by nodes and links as voltages and currents, respectively. Their error correcting capacity (minimum basins of attraction) is decided by the minimum numbers of links of cutset and loop, respectively. Simulation results showing the performance of both types of network are reported
Keywords :
Hopfield neural nets; data compression; image coding; image reconstruction; DH-type CNN; H-type CNN; cellular neural networks; cutset; dual network; duality theory; error correcting capacity; image compression; image regeneration; input information sequences; loop; minimum basins; nonlinear associative networks; retina chips; sparse Hopfield-type neural network; Artificial neural networks; Cellular neural networks; DH-HEMTs; Error correction; Hopfield neural networks; Image coding; Neural networks; Quantization; Retina; Voltage;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.229937
