Tutorial 42 - Histogram equalization and contrast limited adaptive histogram equalization (CLAHE)
This video explains the principles of histogram equalization and CLAHE. It also explains how you can apply these operations to your images in python.
If the image histogram is confined only to a small region (low contrast images), histogram equalization can be used to stretch the histogram to include all ranges. But, this type of stretching may not result in ideal results and gives too bright and too dark regions in the image. This can be especially very bad for images with large intensity variations.
Contrast limited adaptive histogram equalization (CLAHE)
Regular histogram equalization uses global contrast of the image. This results in too bright and too dark regions as the histogram stretches and is not confined to specific region.
Adaptive histogram equalization divides the image into small tiles and within
each tile the histogram is equalized. Tile size is typically 8x8. If the image contains noise, it gets amplified during this process. Therefore,
contrast limiting is applied to limit the contrast below a specific limit. Bilinear interpolation is performed between tile borders.
Code associated with these tutorials can be downloaded from here: https://github.com/bnsreenu/python_fo...