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ADAPTIVE HISTOGRAM

EQUALIZATION

yAngad chahalyAshish Mane

y Dhaval Solanki

y Durgesh kesarkar

y Rupesh gawade

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Histogram Equalization: 8he histogram equalization is an approach to

enhance a given image. The approach is to design

a transformation such that the gray values in theoutput is uniformly distributed in [0, 1] i.e

pout (s) ! 1, 0 e s e1

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Histogram equalisation

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Adaptive histogram equalization:

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yAdaptive histogram equalization is a computerimage processing technique used to improve contrast

in images.y It computes several histograms, each corresponding to

a distinct section of the image, and uses them toredistribute the lightness values of the image.

yAdaptive histogram equalization is considered animage enhancement technique capable of improvingan image's localcontrast, bringing out more detail inthe image.

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Drawback:

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y It produces noise in the images

y This noise can spoil the information content of theimage

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Solution:

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Contrast limited adaptive

histogram equalization(CLAHE):y CLAHE is improved version of AHE

y CLAHE is a technique used to improve the local

contrast of an image.y TheCLAHE algorithm partitions the images into

contextual regions and applies the histogramequalization to each one.

y This evens out the distribution of used grey values andthus makes hidden features of the image more visible.

y The full grey spectrum is used to express the image.

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Reduction of noise :

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y Noise can be reduced while maintaining the highspatial frequency content of the image by applying a

combination ofCL

AHE, median filtration and edgesharpening.

y This technique known as Sequential processing can be

recorded into a user macro for repeat application atany time.

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Filtration methods to avoid noise:

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2D minimum filter:y The minimum filter replaces the value of a pixel by the

smallest value of neighboring pixels covered by aNxNmatrix mask.

y The size of the mask can be adjusted via the input fieldkernel size

yIf applied to a binary label field the minimum filterimplements a so-called erosion operation. It reduces thesize of a segmented region by removing pixels from itsboundary.

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2D maximum filtering:y The maximum filter replaces the value of a pixel by the

largest value of neighboring pixels covered by aNxN

mask.y The size of the mask can be adjusted via the input field

kernel size.

y If applied to a binary label field the maximum filter

implements a so-called dilation operation. It enlargesthe size of a segmented region by adding pixels to itsboundary.

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2D median filtering:y The median filter is a simple edge-preserving

smoothing filter.

yThe filter works by sorting pixels covered by aNxNmask according to their grey value.

y The center pixel is then replaced by the median ofthese pixels, i.e., the middle entry of the sorted list.

y The size of the pixel mask may be adjusted via the textfield labeled kernel size.

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Example:

Original image

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Image after CLADHE:

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Applications:

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yADE can be used to enhance image used to visualize 3-D data

y It is widely used in medicine industry

y It also has some radiological applications

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