IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 8, AUGUST 2006 2303 Gray-Level Grouping (GLG): An Automatic Method for Optimized Image Contrast Enhancement—Part II: The Variations ZhiYu Chen, Senior Member, IEEE, Besma R. Abidi, Senior Member, IEEE, David L. Page, Member, IEEE, and Mongi A. Abidi, Member, IEEE Abstract—This is Part II of the paper, “Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement”. Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conve- niently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images. Index Terms—Contrast enhancement, gray-level grouping, his- togram, noise reduction. I. INTRODUCTION AND RELATED WORK T HIS is Part II of the paper, “Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast En- hancement” [1]. Numerous contrast enhancement techniques exist nowadays. A survey on existing techniques has been presented in Part I. A new automatic contrast enhancement Manuscript received February 1, 2005; revised August 26, 2005. This work was supported in part by the DOE University Research Program in Robotics under Grant DOE-DE-FG02-86NE37968, in part by the DOD/TACOM/NAC/ARC Program R01-1344-18, in part by the FAA/NSSA Program R01-1344-48/49, and in part by the Office of Naval Research under Grant N000143010022. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Hassan Foroosh. The authors are with the Electrical and Computer Engineering Depart- ment, University of Tennessee, Knoxville, TN 37996-2100 USA (e-mail: zychen@utk.edu; besma@utk.edu; dpage@utk.edu; abidi@utk.edu). Digital Object Identifier 10.1109/TIP.2006.875201 technique—gray-level grouping (GLG) was also introduced in Part I. GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement tech- niques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images very well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application requirements, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogrm. Some low-contrast images have noisy backgrounds repre- senting a fairly large percentage of the image area. The high amplitudes of the histogram components corresponding to the noisy image background often prevent the use of conven- tional histogram equalization techniques and the new basic GLG technique, because they would significantly amplify the background noise, rather than enhance the image foreground. For example, Fig. 1(a) shows an original low-contrast X-ray image of a baggage, and Fig. 2(a) its histogram. Fig. 1(b) is the result of its histogram equalization, and Fig. 2(b) the resulting histogram. Due to the high amplitude of the his- togram components corresponding to the noisy background in the original image, the background noise in the output image has been significantly amplified. Since the background histogram components are spread out on the grayscale, the space for other histogram components has been compressed, and as a result, the contrast of the contents in the baggage is decreased instead of increased. Fig. 1(c) shows the result of applying the fast GLG method to the X-ray baggage image, and Fig. 2(c) the resulting histogram. This result is obviously better than the result of histogram equalization because it has less background noise and does result in a contrast increase for the contents of the baggage. However, also due to the large amplitudes of the histogram components corresponding to the noisy background, the background noise has been significantly amplified compared to the original, and the resulting image is not very satisfactory. Therefore, the incapability of enhancing images with a noisy background is a limitation for the basic GLG method. The values of two quality measures, the Tenen- grad criterion and the average pixel distance on the grayscale , are listed in the figure captions. They will be discussed in the next section. Some applications require enhancing part of an image which corresponds to a certain segment of the image histogram, or enhancing different parts of the histogram to different extents. 1057-7149/$20.00 © 2006 IEEE Z. Chen, B. Abidi, D. Page, and M. Abidi, "Gray Level Grouping (GLG): An Automatic Method for Optimized Image Contrast Enhancement - Part II: The Variations," IEEE Trans. on Image Processing, Vol. 15. No. 8, pp. 2303 - 2314, August 2006. 231