Performance Evaluation of Contrast Enhancement Techniques for Digital Images

Histogram equalization (HE) is widely used for contrast enhancement in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. For improving the contrast in digital images, Histogram Equalization (HE) is one of the common methods used for contrast enhancement. But, this technique is not well suited for its implementation in consumer electronics, as this method will introduce visual deterioration such as saturation effect. To overcome this weakness the solution is to preserve the mean brightness of the input image inside the output image. In this paper there is a comparison of HE with Recursively Separated and Weighted Histogram Equalization (RSWHE) and Brightness Preserving Dynamic Histogram Equalization (BPDHE). The essential idea of RSWHE is to segment an input histogram into two or more sub-histograms recursively to modify the sub-histograms by means of a weighting process based on a normalized power law function, and to perform histogram equalization on the weighted sub-histograms independently. RSIHE (Recursive Sub Image Histogram Equalization) and RMSHE (Recursive Mean Separate Histogram Equalization) are some methods similar to RSWHE, but they do not carry out the above weighting process. We show that compared to other existent methods, RSWHE preserves the image brightness more accurately and produces images with better contrast enhancement. It will enhance the image without severe side effects, and at the same time maintain input mean brightness. Comparison is done on the basis of different parameters like Image Brightness Mean (IBM), Image Contrast Standard Deviation (ICSD) and Peak Signal to Noise Ratio (PSNR).