Low Dynamic Range Histogram Equalization (LDR-HE) Via Quantized Haar Wavelet Transform

Abstract

Conventional contrast enhancement methods stretch histogram bins to provide a uniform distribution. However, they also stretch the existing natural noises which cause abnormal distributions and annoying artifacts. Histogram equalization should mostly be performed in low dynamic range (LDR) in which noises are generally distributed in high dynamic range (HDR). In this study, a novel image contrast enhancement method, called low dynamic range histogram equalization (LDR-HE), is proposed based on the Quantized Discrete Haar Wavelet Transform (HWT). In the frequency domain, LDR-HE performs a de-boosting operation on the high-pass channel by stretching the high frequencies of the probability mass function to the nearby zero. For this purpose, greater amplitudes than the absolute mean frequency in the high pass band are divided by a hyper alpha parameter. This damping parameter, which regulates the global contrast on the processed image, is the coefficient of variations of high frequencies, i.e., standard deviation divided by mean. This fundamental procedure of LDR-HE definitely provides a scalable and controlled dynamic range reduction in the histograms when the inverse operation is done in the reconstruction phase in order to regulate the excessive contrast enhancement rate. In the experimental studies, LDR HE is compared with the 14 most popular local, global, adaptive, and brightness preserving histogram equalization methods. Experimental studies qualitatively and quantitatively show promising and encouraging results in terms of different quality measurement metrics such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), Contrast Improvement Index (CII), Universal Image Quality Index (UIQ), Quality-aware Relative Contrast Measure (QRCM), and Absolute Mean Brightness Error (AMBE). These results are not only assessed through qualitative visual observations but are also benchmarked with the state-of-the-art quantitative performance metrics.