Comparative analysis of contrast enhancement techniques between histogram equalization and CNN

Abstract

Contrast enhancement is one of the primary aspects in computer vision. In order to understand the image, the contrast of the image should be clear. In many scenarios, especially in biomedical images, security and surveillance, the visual quality of source images or video is not up to the expected quality. There exist many algorithms such as histogram equalization, genetic algorithms and neural networks to improve the contrast of the images. In this work, we summarized the state of the art and made comparative study among contrast enhancement techniques. Comparisons are done in two cases: one among the histogram based techniques, another between histogram based techniques and method using Cellular Neural Networks (CNN). The method using CNN proved to perform better than the conventional techniques.