CLAHE

Targets:

image

Image Types:uint8, float32

Contrast Limited Adaptive Histogram Equalization: local contrast with clip_limit and tile_grid_size. Good for non-uniform lighting; preserves detail.

CLAHE is an advanced method of improving the contrast in an image. Unlike regular histogram equalization, which operates on the entire image, CLAHE operates on small regions (tiles) in the image. This results in a more balanced equalization, preventing over-amplification of contrast in areas with initially low contrast.

Arguments

clip_limit

tuple[float, float] | float

Controls the contrast enhancement limit.

If a single float is provided, the range will be (1, clip_limit).
If a tuple of two floats is provided, it defines the range for random selection. Higher values allow for more contrast enhancement, but may also increase noise. Default: (1, 4)

tile_grid_size

tuple[int, int]

[8,8]

Defines the number of tiles in the row and column directions. Format is (rows, columns). Smaller tile sizes can lead to more localized enhancements, while larger sizes give results closer to global histogram equalization. Default: (8, 8)

p

float

0.5

Probability of applying the transform. Default: 0.5

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> transform = A.CLAHE(clip_limit=(1, 4), tile_grid_size=(8, 8), p=1.0)
>>> result = transform(image=image)
>>> clahe_image = result["image"]

Notes

Supports only RGB or grayscale images.
For color images, CLAHE is applied to the L channel in the LAB color space.
The clip limit determines the maximum slope of the cumulative histogram. A lower clip limit will result in more contrast limiting.
Tile grid size affects the adaptiveness of the method. More tiles increase local adaptiveness but can lead to an unnatural look if set too high.

References

[{'description': 'Tutorial', 'source': 'https://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html'}, {'description': '"Contrast Limited Adaptive Histogram Equalization."', 'source': 'https://ieeexplore.ieee.org/document/109340'}]

>>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) >>> transform = A.CLAHE(clip_limit=(1, 4), tile_grid_size=(8, 8), p=1.0) >>> result = transform(image=image) >>> clahe_image = result["image"]