CLAHE

Description

Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input image.

    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.

    Args:
        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]): 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): Probability of applying the transform. Default: 0.5

    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.

    Targets:
        image

    Image types:
        uint8, float32

    Example:
        >>> 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"]

    References:
        - https://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html
        - Zuiderveld, Karel. "Contrast Limited Adaptive Histogram Equalization."
          Graphic Gems IV. Academic Press Professional, Inc., 1994.
    

Parameters

• clip_limit: int | tuple[int, int] | float | tuple[float, float] (default: 4)
• tile_grid_size: tuple[int, int] (default: (8, 8))
• p: float (default: 0.5)

Targets

• Image