Abstract

Image enhancement is one of the key techniques in processing quality of images in systems. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image. This technique provides a multitude of choices for improving the visual quality of images. This is the main reason that image enhancement is used in a huge number of applications with important challenges such as noise reduction, degradations, blurring etc. This paper focuses on three contrast enhancement techniques for image enhancement which are: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) which are then compared with the help of the eight (8) quality image measurement metrics which are: i.e. the Mean squared error (MSE), Root Mean squared error (RMSE), Peak signal noise ratio (PSNR), Mean absolute error (MAE), Signal to noise ratio (SNR), Image Quality Index (IQI), Similarity Index (SI) and Pearson Correlation Coefficient (r). The paper concluded that Histogram Equalization (HE), is the one best contrast enhancement technique, as it recorded high percentage values for all the eight (8) quality image measurement metrics. Overall, it was therefore recommended histogram equalization technique should be embedded in any system that processes on images and output them to humans, for making life-changing decisions

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