title: Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement--Part I: the basic method.
abstract: Contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques either often fail to produce satisfactory results for a broad variety of low-contrast images, or cannot be automatically applied to different images, because their parameters must be specified manually to produce a satisfactory result for a given image. This paper describes a new automatic method for contrast enhancement. The basic procedure is to first group the histogram components of a low-contrast image into a proper number of bins according to a selected criterion, then redistribute these bins uniformly over the grayscale, and finally ungroup the previously grouped gray-levels. Accordingly, this new technique is named gray-level grouping (GLG). GLG not only produces results superior to conventional contrast enhancement techniques, but is also fully automatic in most circumstances, and is applicable to a broad variety of images. An extension of GLG, selective GLG (SGLG), and its variations will be discussed in Part II of this paper. SGLG selectively groups and ungroups histogram components to achieve specific application purposes, such as eliminating background noise, enhancing a specific segment of the histogram, and so on. The extension of GLG to color images will also be discussed in Part II.
authors.affiliation: Electrical and Computer Engineering Department, University of Tennessee, Knoxville, TN 37996-2100, USA. zychen@utk.edu
authors.forename: ZhiYu
authors.initials: Z
authors.lastname: Chen
authors.forename: Besma R
authors.initials: BR
authors.lastname: Abidi
authors.forename: David L
authors.initials: DL
authors.lastname: Page
authors.forename: Mongi A
authors.initials: MA
authors.lastname: Abidi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011786:Quality Control