Image Contrast Enhancement by using Histogram Clipping and 2-D Histogram
محورهای موضوعی : Signal Processing; Image Processing
Mahdis Golabian
1
(the Department of Electrical Engineering,Shiraz Branch, Islamic Azad University, Iran, Shiraz)
Azar Mahmoodzadeh
2
(Electrical Engineering,Islamic azad University,Shiraz Branch,Shiraz,Fars,Iran)
Hamed Agahi
3
(Department of Electrical Engineering,Shiraz Branch, Islamic Azad University, Iran, Shiraz)
کلید واژه: Skewness, Contrast Enhancement, Visual machine algorithm, 2-D Histogram, clipped Histogram,
چکیده مقاله :
Several factors are affected images' contrast and eliminated details in images. Therefore, contrast enhancement is a critical process for any visual machine algorithms. To achieve this purpose, in this paper, a novel algorithm based on 2-D histogram and clipped histogram is introduced. To improve the performance of the algorithm, the histogram is divided into three sub-histograms based on mean and standard deviation. For each sub-histogram image, clipped histogram is calculated, separately. The threshold for clipping of histogram is obtained based on median of 2-D histogram of image. Based on the pervious researches we know that the desired distribution for 2-D histogram is Gaussian distribution. Hence, we introduce a novel iterative algorithm for transforming the available histogram to desired histogram. On the other words, our method modifies image histogram to improve its contrast. Our proposed method is based on Skewness, where algorithm is attempted to minimize the absolute value of Skewness. The performance of the algorithm is compared by several algorithms based on different factors. Simulation results indicate the proposed algorithm has the best performance than other algorithms.
Several factors are affected images' contrast and eliminated details in images. Therefore, contrast enhancement is a critical process for any visual machine algorithms. To achieve this purpose, in this paper, a novel algorithm based on 2-D histogram and clipped histogram is introduced. To improve the performance of the algorithm, the histogram is divided into three sub-histograms based on mean and standard deviation. For each sub-histogram image, clipped histogram is calculated, separately. The threshold for clipping of histogram is obtained based on median of 2-D histogram of image. Based on the pervious researches we know that the desired distribution for 2-D histogram is Gaussian distribution. Hence, we introduce a novel iterative algorithm for transforming the available histogram to desired histogram. On the other words, our method modifies image histogram to improve its contrast. Our proposed method is based on Skewness, where algorithm is attempted to minimize the absolute value of Skewness. The performance of the algorithm is compared by several algorithms based on different factors. Simulation results indicate the proposed algorithm has the best performance than other algorithms.
