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http://dx.doi.org/10.9717/kmms.2012.15.6.748

Image Histogram Equalization Based on Gaussian Mixture Model  

Jun, Mi-Jin (계명대학교 대학원 미디어아트학과)
Lee, Joon-Jae (계명대학교 게임모바일콘텐츠학과)
Publication Information
Journal of Korea Multimedia Society / v.15, no.6, 2012 , pp. 748-760 More about this Journal
Abstract
In case brightness distribution is concentrated in a region, it is difficult to classify the image features. To solve this problem, we apply global histogram equalization and local histogram equalization to images. In case of global histogram equalization, it can be too bright or dark because it doesn't consider the density of brightness distribution. Thus, it is difficult to enhance the local contrast in the images. In case of local histogram equalization, it can produce unexpected blocks in the images. In order to enhance the contrast in the images, this paper proposes a local histogram equalization based on the Gaussian Mixture Models(GMMs) in regions of histogram. Mean and variance parameters in each regions is updated EM-algorithm repeatedly and then ranges of equalization on each regions. The experimental results performed with image of various contrasts show that the proposed algorithm is better than the global histogram equalization.
Keywords
Histogram Equalization; Gaussian Mixture Model; Contrast Enhancement;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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