Adaptive Histogram Equalization

Adaptive histogram equalization (AHE) is an image processing technique where several image histograms are generated by moving a sliding window across the image and use the generated histograms to redistribute the image intensities in the image. AHE is useful for improving local contrast and enhancing local edges.

Sliding window size: Size (in pixels) of square sliding window. This determines the local area of the image used to calculate each intensity histogram distribution.

Clip factor: Factor used to reduce the contrast enhancement in background regions with uniform graylevel, where a full equalize only would enhance noise. Clipping is performed by counting histogram classes only up to a certain level:

Clipfactor = 0: no clipping (noise may be severely enhanced)

Clipfactor = 100: Histogram cells clipped at 3*(median histogram)

Clipfactor = very large: Maximum clipping, cells count at most 1

Sample cases:

Figure 1 Original image (left). AHE processed with Window size= 30 and Clipfactor = 300 (centre) and with Clipfactor=100 (right)

Figure 2. Original contrast enhanced T1-weighted SE image (left) and AHE processed (right) with sliding window size=10 and Clip factor=300