Adaptive histogram equalization with cellular neural networks

Adaptive histogram equalization (AHE), a method of contrast enhancement which is sensitive to local spatial information in an image, has demonstrated effectiveness in many applications However, this technique is obviously computationally intensive. In this paper we present two computational methods designed to Pt well onto the locally interconnected array computer architecture of cellular neural networks (CNNs [1, 2]). CNNs are well known for their image processing capabilities also when processing grey-scale medical images and images taken in 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 to reach this goal we can use this method of AHE in a pre-processing step.