_clahe(image, kernel_size, clip_limit, nbins)

Parameters

image : (N1,...,NN) ndarray

Input image.

kernel_size: int or N-tuple of int :

Defines the shape of contextual regions used in the algorithm.

clip_limit : float

Normalized clipping limit (higher values give more contrast).

nbins : int

Number of gray bins for histogram ("data range").

Returns

out : (N1,...,NN) ndarray

Equalized image.

The number of "effective" graylevels in the output image is set by `nbins`;

selecting a small value (eg. 128) speeds up processing and still produce

an output image of good quality. The output image will have the same

minimum and maximum value as the input image. A clip limit smaller than 1

results in standard (non-contrast limited) AHE.

Contrast Limited Adaptive Histogram Equalization.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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File: /skimage/exposure/_adapthist.py#101
type: <class 'function'>
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