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We propose a novel Histogram Equalization (HE) to improve contrast of images. As most existing HE methods still suffer from over-enhancement caused by a quantum jump, the proposed method focuses on robustness to deal with the problem in various image conditions. To achieve the goal, we adjust the curve shape of the output mapping function by properly combining the curve shape of the null mapping function and that of the normal mapping function from the conventional HE according to the weighting value. Experimental results show that the proposed method well endures difficult conditions and provides moderate image quality.

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There are various algorithms evaluating a threshold for image segmentation. Among them, Otsu's algorithm sets a threshold based on the histogram. It finds the between-class variance for all over gray levels and then sets the largest one as Otsu's optimal threshold, so we can see that Otsu's algorithm requires a lot of the computation. In this paper, we improved the amount of computational needs by using estimated Otsu's threshold rather than computing for all the threshold candidates. The proposed algorithm is compared with the original one in computation amount and accuracy. we confirm that the proposed algorithm is about 29 times faster than conventional method on single processor and about 4 times faster than on parallel processing architecture machine.