An Artificial Neural Network based Adaptive Histogram Equalization Algorithm for Enhancement of Low Contrast Images

The implementation of an image contrast enhancement algorithm along with artificial intelligence techniques can have various applications besides modern photography. It basically ameliorates the quality of low contrast images. The main focus of this research is developing a new image contrast enhancement method that combines the concept of artificial intelligence and histogram equalization techniques to provide a contrast distribution for the low contrast images by utilizing the classifier to prevent data loss from images. In this research an ANN based AHE algorithm for enhancement of low contrast images is proposed. The main objectives of this research is to study the existing digital image contrast enhancement techniques to find out the exact problems and to classify the level of contrast in a digital image as low or high, so as to ascertain whether enhancement is required or not. The concept of ANN with AHE is used here to find out the contrast level of the image before processing for contrast enhancement. For validation of the proposed ANN-AHE algorithm, a comparison with the existing techniques are performed on the behalf of performance parameters such as PSNR, MSE, Entropy, QI, QRCM, CQE, SSIM and Computational Time. The simulation of the proposed model is performed in MATLAB 2016a with the help of image processing and artificial neural network toolbox.