histogram equalization

Improved image contrast is one of the most important operations used in image processing and visual processing applications. Histogram equalization is one of the most powerful ways to increase the contrast and improve the quality of digital images. However, this method usually causes an excessive increase in contrast and a loss of part of the image details. Many problems have been resolved in the past two decades in order to overcome the many problems of histogram equalization. In this paper, we have reviewed, categorized and compared the methods proposed in the field of histogram equalization for twenty years.

In this paper, a powerful contrast enhancement algorithm based on the histogram equalization, called triple dynamic clipped histogram equalization (TDCHE) is introduced. The proposed method consists of four main processes including partitioning, clipping, mapping and equalization. At first, the input image histogram is partitioned into three portions with a same number of pixels. Next, the histogram clipping process is performed by comparing the clipped threshold level with the average intensities occurred on each sub-histogram. Then, each sub-histogram is mapped to a new dynamic range using simple calculations and finally, equalization process of each histogram is independently accomplished. The proposed TDCHE technique is presented to achieve multiple objectives of maximum average information content (entropy), enhancement ratio control and maintaining the reasonable brightness. In addition, this method leads to a natural enhancement in the output image by providing sharp images, while preserving maximum details far from unnatural phenomena such as intensity saturation and noise enhancement. Evaluation of the proposed TDCHE method performance in terms of information content as well as visual quality shows perceived superiority of the proposed algorithm in comparison to the previously presented methods based on histogram equalization.