Contrast Enhancement of Sports Images Using Two Comparative Approaches

Abstract

In this paper we have proposed two co mparative approaches for the contrast enhancement of dark sports images. The contrast of any image is a very important characteristic wh ich decides the quality of image. Lo w contrast images occur often due to poor or non uniform lighting conditions and sometimes due to the non linearity or small dynamic range of the imaging system. Enhancing the contrast of sports images is of importance since it is difficult to analyze the performance of the team or player with a poor quality image. Though several methods are proposed for gray scale images, enhancing the contrast of color images is a comp licated process. In this paper we have proposed two comparative approaches for the contrast enhancement of color images and have compared their perfo rmance against the standard histogram equalization method. First method is contrast enhancement of color images using fuzzy rule based method and the second method is using modified sigmo id function. Color images cannot be processed directly hence a suitable color model is chosen for processing and the proposed methods are imp lemented. Fo r both the approaches the color images are split into RGB p lanes and the proposed operation is performed on each plane and finally the planes are concatenated to obtain the enhanced image. Performance of the proposed methods is measured using a factor known as Measure of Contrast and the comparison is represented graphically. Experimental results prove that of the two methods proposed, contrast enhancement using modified sigmoid function provides the highest measure of contrast and can be effectively used for further analysis of sports color images.

A new algorithm for improving the low contrast of computed tomography images using tuned brightness controlled single-scale Retinex

“…The sigmoid is a nonlinear intensity enhancement function, which has been used in many imaging applications. The standard sigmoid function is described as follows (Kannan et al ., ): Where, g is the degraded image and α is a constant parameter that controls the enhancement of the function. In this study, an ameliorated version of the latter function is used to provide better intensity results, which is described as: Where, γ is an adjusting constant that by default, γ = 2.…”

Review and Analysis of Various Image Enhancement Techniques

“…Kannan P., Deepa S. and Ramakrishnan [2] presented two approaches for the enhancement of dark sports images. Low contrast images may occur because of poor lightning conditions or small dynamic range of imaging systems.…”

Segmentation Method of Breast Masses on Ultrasonographic Images Using Level Set Method Based on Statistical Model

“…Figure 1 shows a schematic diagram of our proposed method for the segmentation of region of breast masses. The contrast of mass region in ultrasonographic image was first enhanced using a modified sigmoid function [21]. The region of breast mass was segmented by the novel level set method with the energy function consisted of three energy terms: a region-based term, edge-based term, and regularizing term.…”

“…c was a contrast factor, whereas th was the threshold value. In the modified sigmoid function, it is possible to change the amount of lightening and darkening to control the overall contrast enhancement by adjusting c and th [21]. Kannan et al reported that an optimal threshold value of th was between 0.3 and 0.5 [21].…”