Histogram Equalization using Gamma Transformation
Soyoung Chung, Min Gyo Chung · 정보과학회 컴퓨팅의 실제 논문지 · 2014
Histogram equalization generally has the disadvantage that if the distribution of the gray level of an image is concentrated in one place, then the range of the gray level in the output image is excessively expanded, which then produces a visually unnatural result. However, a gamma transformation can reduce such unnatural appearances since it operates under a nonlinear regime. Therefore, this paper proposes a new histogram equalization method that can improve image quality by using a gamma transformation. The proposed method 1) derives the proper form of the gamma transformation by using the average brightness of the input image, 2) linearly combines the earlier gamma transformation with a CDF (Cumulative Distribution Function) for the image in order to obtain a new CDF, and 3) to finally perform histogram equalization by using the new CDF. The experimental results show that relative to existing methods, the proposed method provides good performance in terms of quantitative measures, such as entropy, UIQ, SSIM, etc., and it also naturally enhances the image quality in visual perspective as well.