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Dynamic histogram equalization based on gray level labeling

Histogram equalization is one of the well-known methods for contrast enhancement. However, the conventional contrast enhancement methods based on histogram equalization still show some problems such as washed out appearance and gradation artifact. To overcome such drawbacks, we propose a novel dynamic histogram equalization method based on gray level labeling method. The main contribution of the proposed method is to expand the dynamic range of the subhistogram up to the entire dynamic range of the input image while the intensity orders of adjacent pixels are preserved. The proposed method first decomposes the image histogram into a number of sub-histograms based on gray level labeling method. A full dynamic range of input gray level is assigned to each sub-histogram and each transform function is calculated based on the bi-histogram equalization method. Finally, a contrast enhanced pixel value is the weighted average of the results from each transform function. Experimental results show that the proposed method produces better contrast enhanced images than several histogram equalization based methods without introducing several side effects.