An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement

Abstract

Adaptive histogram equalization (AHE) and intensity windowing have been compared using psychophysical observer studies. Experienced radiologists were shown clinical CT (computerized tomographic) images of the chest. Into some of the images, appropriate artificial lesions were introduced; the physicians were then shown the images processed with both AHE and intensity windowing. They were asked to assess the probability that a given image contained the artificial lesion, and their accuracy was measured. The results of these experiments show that for this particular diagnostic task, there was no significant difference in the ability of the two methods to depict luminance contrast; thus, further evaluation of AHE using controlled clinical trials is indicated.

Adaptive Sigmoid Function to Enhance Low Contrast Images

“…The basic idea of HE method is to re-map the gray levels of an image based on the image's gray levels cumulative density function. HE flattens and stretches the dynamic range of the resultant image histogram and as a consequence, it enhances the contrast of the image and gives an overall contrast improvement [11]. However, HE is rarely employed in consumer electronic applications such as video surveillance, digital camera, and television since HE tends to introduce some annoying artifacts and unnatural enhancement, including intensity saturation effect.…”

Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns

“…We tested a technique called Contrast Limited Adaptive Histogram Equalization (CLAHE) [20], which is a variant of Adaptive Histogram Equalization (AHE) [21]. AHE computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.…”

Correction of Color Cast Images using Histogram Equalization of Local Area

“…Histogram equalization [6][7][8][9][10] is a technique for adjusting image intensities to enhance contrast. f is represented as by matrix of integer pixel intensity from 0 to 1, and that the specified image.…”