DocumentCode :
1577893
Title :
Adaptive histogram equalization with cellular neural networks
Author :
Csapodi, Márton ; Roska, Tamás
Author_Institution :
Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary
fYear :
1996
Firstpage :
81
Lastpage :
86
Abstract :
Adaptive histogram equalization (AHE), a method of contrast enhancement which is sensitive to local spatial information in image, has demonstrated its effectiveness in many applications. However, this technique is computationally intensive. In this paper we present two computational methods designed to fit well onto the locally interconnected array computer architecture of cellular neural networks (CNNs). CNNs are well known for their image processing capabilities, specially for grey-scale medical images and images of a natural scene. In many applications it would be very useful if the operation of a template or a complex analogic algorithm were highly illumination independent. Our results suggest that we can achieve this goal by using the AHE method in a pre-processing step
Keywords :
cellular neural nets; computer vision; image enhancement; iterative methods; piecewise constant techniques; CNNUM; adaptive histogram equalization; cellular neural networks; contrast enhancement; grey-scale medical images; image processing; iterative method; local spatial information; neural net architecture; Adaptive equalizers; Adaptive systems; Application software; Biomedical imaging; Cellular neural networks; Computer architecture; Computer networks; Design methodology; Histograms; Image processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cellular Neural Networks and their Applications, 1996. CNNA-96. Proceedings., 1996 Fourth IEEE International Workshop on
Conference_Location :
Seville
Print_ISBN :
0-7803-3261-X
Type :
conf
DOI :
10.1109/CNNA.1996.566497
Filename :
566497
Link To Document :
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