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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