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 |