TY - JOUR
T1 - An image pixel based variational model for histogram equalization
AU - Wang, Wei
AU - Chen, Chuan
AU - Ng, Michael K.
N1 - Funding Information:
Research of Wei Wang is supported by National Natural Science Foundation of China (Grant No. 11201341). Research of Michael K. Ng is supported by RGC GRF Grant Nos. 202013, 12301214 and HKBU FRG Grant No. FRG2/13-14/079.
Funding Information:
Research supported by National Natural Science Foundation of China (Grant No. 11201341 ), RGC GRF Grant Numbers 202013 , 12301214 and HKBU FRG Grant Number FRG2/13-14/079 .
PY - 2016/1/1
Y1 - 2016/1/1
N2 - In this paper, we develop an image pixel based histogram equalization model for image contrast enhancement. The approach is to propose a variational model containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform. This idea is different from existing histogram equalization algorithms where a histogram based on the input image is constructed, a mapping is determined to output a uniform histogram and then the pixel values of the input image are adjusted based on the mapping. In the variational model, a mean brightness term is incorporated to preserve the brightness of the input image, and a geometry constraint can also be added to keep the geometry structure of the input image. Theoretically, the existence of the minimizer of the proposed model, and the convergence of the proposed algorithm are given. Experimental results are reported to demonstrate that the performance of the proposed model are competitive with the other testing histogram equalization methods for several testing images.
AB - In this paper, we develop an image pixel based histogram equalization model for image contrast enhancement. The approach is to propose a variational model containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform. This idea is different from existing histogram equalization algorithms where a histogram based on the input image is constructed, a mapping is determined to output a uniform histogram and then the pixel values of the input image are adjusted based on the mapping. In the variational model, a mean brightness term is incorporated to preserve the brightness of the input image, and a geometry constraint can also be added to keep the geometry structure of the input image. Theoretically, the existence of the minimizer of the proposed model, and the convergence of the proposed algorithm are given. Experimental results are reported to demonstrate that the performance of the proposed model are competitive with the other testing histogram equalization methods for several testing images.
KW - Algorithm
KW - Alternating minimization
KW - Contrast enhancement
KW - Energy functional
KW - Euler Lagrange equation
KW - Histogram equalization
KW - Histogram transfer
KW - Variational approach
UR - http://www.scopus.com/inward/record.url?scp=84948160274&partnerID=8YFLogxK
U2 - 10.1016/j.jvcir.2015.10.019
DO - 10.1016/j.jvcir.2015.10.019
M3 - Journal article
AN - SCOPUS:84948160274
SN - 1047-3203
VL - 34
SP - 118
EP - 134
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
ER -