DocumentCode :
295780
Title :
An auto-invertible neural network for image halftoning and restoration
Author :
Yue, Tai- Wen ; Chen, Guo-Tai
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Tatung Inst. of Technol., Taipei, Taiwan
Volume :
3
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
1450
Abstract :
In this paper, we apply the so-called Q´tron NN (neural network) paradigm to perform image halftoning and the `associated´ image restoration. These processes are considered to be located at the different sides of the same process. On one side, the process converts a grey-tone image into a binary image, i.e., halftoning. On the other side, the process just performs the inverse, i.e., it restores a binary image to a grey-tone image. One will see that such an auto-associativity regarding the two tightly correlated images is one of the important features of a Q´tron NN. Experimental results are presented to demonstrate that the resulting quality of images is quite satisfactory
Keywords :
correlation theory; image restoration; neural nets; Q´tron neural network; Q-state neurons; auto-associativity; auto-invertible neural network; binary image; grey-tone image; image halftoning; image restoration; Cellular neural networks; Computer science; Image coding; Image converters; Image restoration; Low pass filters; Neural networks; Neurons; Pixel; Printers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.487373
Filename :
487373
Link To Document :
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