Recursive sub-image histogram equalization applied to gray scale images
Pattern Recognition Letters
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
An image contrast enhancement method based on genetic algorithm
Pattern Recognition Letters
Contrast enhancement using brightness preserving bi-histogram equalization
IEEE Transactions on Consumer Electronics
Image enhancement based on equal area dualistic sub-image histogram equalization method
IEEE Transactions on Consumer Electronics
IEEE Transactions on Consumer Electronics
Minimum mean brightness error bi-histogram equalization in contrast enhancement
IEEE Transactions on Consumer Electronics
A Dynamic Histogram Equalization for Image Contrast Enhancement
IEEE Transactions on Consumer Electronics
Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement
IEEE Transactions on Consumer Electronics
Brightness preserving weight clustering histogram equalization
IEEE Transactions on Consumer Electronics
IEEE Transactions on Consumer Electronics
IEEE Transactions on Consumer Electronics
Image sharpening using sub-regions histogram equalization
IEEE Transactions on Consumer Electronics
IEEE Transactions on Image Processing
Hi-index | 12.05 |
The current major theme in contrast enhancement is to partition the input histogram into multiple sub-histograms before final equalization of each sub-histogram is performed. This paper presents a novel contrast enhancement method based on Gaussian mixture modeling of image histograms, which provides a sound theoretical underpinning of the partitioning process. Our method comprises five major steps. First, the number of Gaussian functions to be used in the model is determined using a cost function of input histogram partitioning. Then the parameters of a Gaussian mixture model are estimated to find the best fit to the input histogram under a threshold. A binary search strategy is then applied to find the intersection points between the Gaussian functions. The intersection points thus found are used to partition the input histogram into a new set of sub-histograms, on which the classical histogram equalization (HE) is performed. Finally, a brightness preservation operation is performed to adjust the histogram produced in the previous step into a final one. Based on three representative test images, the experimental results demonstrate the contrast enhancement advantage of the proposed method when compared to twelve state-of-the-art methods in the literature.