A Novel Method of Determining Parameters for Contrast Limited Adaptive Histogram Equalization

Abstract

Histogram equalization, which stretches the dynamic range of intensity, is the most common method for enhancing the contrast of image. Contrast limited adaptive histogram equalization(CLAHE), proposed by K. Zuierveld, has two key parameters: block size and clip limit. These parameters mainly control image quality, but have been heuristically determined by user. In this paper, we propose a novel method of determining two parameters of CLAHE using entropy of image. The key idea is based on the characteristics of entropy curves: clip limit vs entropy and block size vs entropy. Clip limit and block size are determined at the point with maximum curvature on entropy curve. Experimental results show that the proposed method improves images with very low contrast.

Color Edge Detection Using the Normalization Anisotropic Gaussian Kernel and Multichannel Fusion

“…The This method relies on two essential parameters: the number of tiles and the contrast limiting factor. In the processes of CLAHE for BSDS datasets, the number of tiles is 10*10 and the contrast limiting factor determined by iteration method while entropy of image get the maximal value [26]. The result of a sample image after processing is shown in Fig.…”