Contrast enhancement based on a novel homogeneity measurement

Contrast enhancement is an important issue in image processing, pattern recognition, and computer vision. There are two contrast enhancement methods: the indirect method and the direct method. The indirect method is not efficient and effective since it only stretches the global distribution of the intensity. The direct method defines a measurement of the contrast and uses it to enhance the contrast.; In this thesis, we defined the contrast based on a novel measurement of homogeneity because homogeneity is related to the local information of an image and reflects how uniform an image region is. We define homogeneity based on five components: edge value, standard deviation, entropy, skewness, and kurtosis.; We have conducted experiments on many images. The experimental results demonstrate that the proposed algorithm is very effective in contrast enhancement as well as in preventing overenhancement.; The major advantages of our proposed method are due to the following factors: (1) The novel definition of homogeneity makes the contrast enhancement more adaptive and effective. (2) The determination of amplification constant is dependent on the nature of the original image. (3) The measurement of homogeneity and amplification constant is based on both local and global information.