Interactive Java Tutorials
Levels Adjustment in Digital Images
The tonal range of a digital image is related to the amount of contrast present in the image, with a broad tonal range producing good contrast, and a narrow tonal range indicative of poor contrast. Levels adjustment is a digital image enhancement algorithm that can substantially improve the tonal range of a digital image, thereby increasing overall image contrast.
This interactive tutorial explores the image enhancement technique of levels adjustment on full-color digital images. The tutorial initializes with a randomly selected specimen image (captured in the microscope) appearing in the window entitled Specimen Image. Each specimen name includes, in parentheses, an abbreviation designating the contrast mechanism employed in obtaining the image. The following nomenclature is used: (FL), fluorescence; (BF), brightfield; (DF), darkfield; (HMC) Hoffman modulation contrast; (DIC) differential interference contrast; and (POL), polarized light. Visitors will note that specimens captured using the various techniques available in optical microscopy behave differently during image processing in the tutorial.
To operate the tutorial, select a specimen image from the Choose A Specimen pull-down menu, and translate the position of the Level Amount slider to adjust the tonal range and contrast of the specimen. Each time a new specimen is selected, a random color cast or reduction in contrast is added by the tutorial software. The Leveling Algorithm radio button controls can be utilized to toggle between two available types of leveling algorithms (RGB and HSI) reviewed by this tutorial. The RGB histogram of the leveled image is displayed in the Image Histogram window, which appears adjacent to the specimen image window. Visitors should explore the effects of levels adjustment on the appearance of the various specimen images available in the tutorial.
The Image Histogram window indicates the tonal range of each channel in the specimen image, which is defined as the numeric brightness range extending from the minimum brightness level to the maximum brightness level. Regions of the input pixel brightness range that are not covered by the histogram result because no pixels in the image have those brightness values. A full tonal range indicates the best possible contrast.
Levels adjustment enhances a digital image by artificially increasing its tonal range. The basic and most simple form of the levels adjustment algorithm operates by first finding the centroid or center of mass in the histogram for each color channel. Each channel of the image is then processed in order to stretch the histogram outwards around the centroid. Some implementations of the algorithm allow the user to specify a center point around which to stretch the histogram rather than using the centroid. It is also possible to implement a form of the algorithm that enables the user to specify shadow and highlight points to control the range of the histogram stretch. In the tutorial, the algorithm operates by stretching a variable percentage of the histogram mass located around the centroid. The percentage that is indicated above the Level Amount slider represents the proportion of the histogram that is being lost, or clipped, at the upper and lower extremes of the dynamic range as the histogram is stretched.
Levels adjustment can dramatically improve the tonal range of a digital image having either a color cast or serious contrast problems. However, applying the level algorithm to an image already having a good tonal range and contrast can degrade the quality of the image, because leveling can introduce a color cast or tinge. Prior to application of the algorithm, the target digital image should be carefully examined to determine if a color cast is present.
Interactive lookup-table modification techniques can also be used to remove a color cast from a leveled image. An alternative approach to levels adjustment, which avoids the color cast problem, is to apply a histogram leveling algorithm to the intensity component of the HSI form of the image. Because this algorithm works by adjusting levels for the intensity component only (the hue component remains unmodified), a color cast will never appear during image processing.
Kenneth R. Spring - Scientific Consultant, Lusby, Maryland, 20657.
John C. Russ - Materials Science and Engineering Department, North Carolina State University, Raleigh, North Carolina, 27695.
Matthew J. Parry-Hill, Thomas J. Fellers, and Michael W. Davidson - National High Magnetic Field Laboratory, 1800 East Paul Dirac Dr., The Florida State University, Tallahassee, Florida, 32310.
Questions or comments? Send us an email.
© 1998-2015 by Michael W. Davidson and The Florida State University. All Rights Reserved. No images, graphics, scripts, or applets may be reproduced or used in any manner without permission from the copyright holders. Use of this website means you agree to all of the Legal Terms and Conditions set forth by the owners.
This website is maintained by our