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Table 1 Nomenclature of methods evaluated in this work

From: Image enhancement using local intensity distribution equalization

Technique

Reference

Brief description

HE

[8]

Standard histogram equalization

AHE

This work

Integral image based AHE

LIDE-G

This work

Parametric local equalization with Gaussian model

LIDE-L

This work

Parametric local equalization with Laplacian model

LIDE-GMM

This work

Parametric local equalization with Gaussian mixture model

LIDE-LMM

This work

Parametric local equalization with Laplacian mixture model

DHE

[21]

Recursive histogram partition using peaks and valleys

MHE

[12]

Histogram partition using dynamic programming algorithm

ESIHE

[16]

Histogram partition using exposure level of input image

HEGMM

[29]

A Gaussian mixture model is fitted to the global histogram first. Then we use the information from the mixture components to partition the histogram. Parametric equalization is applied to each interval using information from the corresponding Gaussian component

BPHEME

[23]

Histogram matching using target distribution obtained by maximizing entropy criterion under average intensity preserving constraint

FHSABP

[24]

Histogram matching using target distribution obtained by minimizing the difference to uniform distribution under average intensity preserving constraint