Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving

Multi-Histogram Equalization Methodsfor Contrast Enhancement and Brightness Preserving

David Menotti, Laurent Najman, Jacques Facon, and Arnaldo de A. ArajoIEEE Transactions on Consumer Electronics, Vol. 53, No. 3, AUGUST 2007

Histogram equalization()

Histogram equalization(2)

Change the mean brightness of the image to the middle level of the gray_level range

Multi-Histogram equalizationConsists of decomposing the input image into several sub-images,and then applying the Histogram equalization to each one

natural numbers integer numbers is a sub set of0 x < m , 0 y < n ,

Image I: to L={0,..., L1} L is typically 256 ,a point(x,y) , l=I(x,y) is called the level of the point (x, y) in I .0 ls lf

Shannon's Entropy ( )

Minimum Within-Class Variance MHE (MWCVMHE)

Minimum Middle Level Squared Error MHE (MMLSEMHE).(ls+lf)/2, O[ls,lf ]

middle value of the image

Classical HE method : uniform histogram of the output image

,ls l lf

Other MethodBrightness Bi-HE Method (BBHE) Dualistic Sub-Image HE Method (DSIHE)Minimum Mean Brightness Error Bi-HE Method(MMBEBHE)Recursive Mean-Separate HE Method (RMSHE)

TEST RESULTS

TEST RESULTS