CLAHE

In Image Processing, CLAHE stands for Contrast Limited Adaptive Histogram Equalization. CLAHE is a technique used to improve the local contrast of an image. It is a generalization of adaptive histogram equalization and ordinary histogram equalization. CLAHE was developed^[1] to prevent the overamplification of noise that adaptive histogram equalization can give rise to.

Contrast limiting and CLHE

CLAHE differs from ordinary adaptive histogram equalization (AHE) in its contrast limiting. This feature can also be applied to global histogram equalization, giving rise to contrast-limited histogram equalization (CLHE), which is rarely used in practice. In the case of CLAHE, the contrast limiting procedure has to be applied for each neighbourhood from which a transformation function is derived.

Adaptive histogram equalization works by applying to each pixel a transformation function that is proportional to the cumulative distribution function (CDF) of its neighbourhood in the original image. When the image region containing its neighbourhood is fairly homogeneous, its histogram will be strongly peaked, and the transformation function will map a narrow range of pixel values to the whole range of the result image. This causes AHE to overamplify small amounts of noise in largely homogeneous regions of the image.

As its name suggests, CLAHE remedies this drawback by limiting the contrast enhancement of AHE. The contrast amplification in the vicinity of a given pixel value is given by the slope of the transformation function, which is proportional to the slope of the CDF and therefore to the value of the histogram at that pixel value. CLAHE limits the amplification by clipping the histogram at a predefined value before computing the CDF. This limits the slope of the CDF and therefore of the transformation function. The value at which the histogram is clipped, the so-called clip limit, depends on the normalization of the histogram and thereby on the size of the neighbourhood region. Common values limit the resulting amplification to between 3 and 4.

According to the paper that first presented CLAHE^[1], it is advantageous not to discard the part of the histogram that exceeds the clip limit. Rather, the authors suggest a recursive procedure in which the excess is redistributed equally among all histogram bins, and the histogram is clipped again to limit those bins which the redistribution has pushed over the limit again.

References

1. ^ ^{a b} Pizer, S. M., Amburn, E. P., Austin, J. D., et al.: Adaptive Histogram Equalization and Its Variations. Computer Vision, Graphics, and Image Processing 39 (1987) 355­-368.

External links

• Example images demonstrating the effect of CLAHE at the Max Planck Institute of Molecular Cell Biology and Genetics
• A tutorial on CLAHE
• An example implementation of CLAHE in ANSI C