Image illumination enhancement with an objective no-reference measure of illumination assessment based on Gaussian distribution mapping - Anbarjafari, Gholamreza; Jafari, Adam; Jahromi, Mohammad Naser Sabet; Ozcinar, Cagri; Demirel, Hasan (Elsevier - Division Reed Elsevier India Pvt Ltd, 2015)
Illumination problems have been an important concern in many image processing applications. The pattern of the histogram on an image introduces meaningful features; hence within the process of illumination enhancement, it is important not to destroy such information. In this paper we propose a method to enhance image illumination using Gaussian distribution mapping which also keeps the information laid on the pattern of the histogram on the original image. First a Gaussian distribution based on the mean and standard deviation of the input image will be calculated. Simultaneously a Gaussian distribution with the desired mean and standard deviation will be calculated. Then a cumulative distribution function of each of the Gaussian distributions will be calculated and used in order to map the old pixel value onto the new pixel value. Another important issue in the field of illumination enhancement is absence of a quantitative measure for the assessment of the illumination of an image. In this research work, a quantitative measure indicating the illumination state, i.e. contrast level and brightness of an image, is also proposed. The measure utilizes the estimated Gaussian distribution of the input image and the Kullback-Leibler Divergence (KLD) between the estimated Gaussian and the desired Gaussian distributions to calculate the quantitative measure. The experimental results show the effectiveness and the reliability of the proposed illumination enhancement technique, as well as the proposed illumination assessment measure over conventional and state-of-the-art techniques. (C) 2015 Karabuk University. Production and hosting by Elsevier B.V.
Keywords: Illumination enhancement; Gaussian distribution mapping; Illumination assessment measure; Image processing; Kullback-Leibler divergence; Image enhancement