Local area histogram equalization based multispectral image enhancement from clustering using competitive Hopfield neural network

One of important issues for enhancing image based on local area histogram equalization (LHE) is a clustering or segmenting technique. That is, the more the accuracy of separating image into specified classes is needed, the better the performance of enhancement is. As mentioned objective, in this paper, the competitive Hopfield neural network (CHNN) is then proposed for clustering to the LHE based image enhancement. By using simulated image, standard image and mutispectral image from Landsat 7 satellite, experimental results are shown in both accuracy of clustering and variance of the enhanced image. The criteria for a good enhancement algorithm is that it can give high variance in detail area, low variance in smooth and edge areas. Also comparing the variance of the enhanced image by both LHE and global area histogram equalization (GHE) methods shows that one from LHE outperforms. In addition, the enlarged image from small area is shown clearly by visualization. All results compare with the conventional methods such as fuzzy c-means (FCM).