RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement

Abstract

A large number of color image enhancement methods are based on the methods for grayscale image enhancement in which the main interest is contrast enhancement. However, since colors usually have three attributes, including hue, saturation and intensity of more than only one attribute of grayscale values, the naive application of the methods for grayscale images to color images often results in unsatisfactory consequences. Conventional hue-preserving color image enhancement methods utilize histogram equalization (HE) for enhancing the contrast. However, they cannot always enhance the saturation simultaneously. In this paper, we propose a histogram specification (HS) method for enhancing the saturation in hue-preserving color image enhancement. The proposed method computes the target histogram for HS on the basis of the geometry of RGB (rad, green and blue) color space, whose shape is a cube with a unit side length. Therefore, the proposed method includes no parameters to be set by users. Experimental results show that the proposed method achieves higher color saturation than recent parameter-free methods for hue-preserving color image enhancement. As a result, the proposed method can be used for an alternative method of HE in hue-preserving color image enhancement.

Hue-Preserving Saturation Improvement in RGB Color Cube

“…Figure 6 d shows the result of HS combined with Naik and Murthy’s method, where the contrast is suppressed while the saturation is improved, compared with Figure 6 a. Figure 6 e of Yang and Lee’s method shows a more colorful image than Figure 6 d. Figure 6 d,e coincides with the results of Inoue’s method [ 15 ]. Figure 6 f of the proposed method shows further improved saturation than Figure 6 e, specifically for the regions of the facial skin and curtain in the background.…”

“…As a reasonable choice for the target histogram, we have proposed an RGB color cube-based method as follows [ 15 ]: We compute the area of the cross section of a normalized RGB color cube and an equi-intensity plane , where , , and denotes the Euclidean norm, as a function of : from which the target histogram is given by the following: for . We also have the integral of as follows: from which the target cumulative histogram is given by the following: for .…”

“…We can see that the above method does not increase the saturation defined by the following: which is the distance from the intensity axis joining the black and white vertices to [ 12 ], where I denotes the identity matrix. We have proved that the following equations hold for any nonnegative numbers and [ 15 ]: …”

“…We have proved that the saturation given by Yang and Lee’s method is greater than or equal to that given by Naik and Murthy’s method [ 15 ]. However, as described on line 9 in Algorithm 2, Yang and Lee’s method directly uses Naik and Murthy’s method for .…”

“…We assume that the hue, saturation and intensity are defined in the HSI color model [ 12 ]. We first compute the target intensities for all pixels by histogram equalization (HE) [ 13 ] or histogram specification (HS), which is also called histogram matching [ 14 ], where an RGB color cube-based method [ 15 ] is adopted for further improvement of saturation. Then, we project each color in a given input image onto a surface in an RGB color space, which bisects the RGB color cube into dark and bright parts.…”