Grayscale Image Complement

Grayscale Image Complement - Java Tutorial

Grayscale image complement operations are useful for enhancing the visibility of subtle brightness variations among gray levels in regions of a digital image where fine details are obscured. This interactive tutorial explores the effects of grayscale complement operations on grayscale digital images and their histograms.

The tutorial initializes with a randomly selected specimen image (captured in the microscope) appearing in the left-hand 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; (PC), phase contrast; (DIC), differential interference contrast (Nomarski); (HMC), Hoffman modulation 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.

Adjacent to the Specimen Image window is the Grayscale Histogram window that displays a gray-level histogram bar graph produced from the specimen image. To operate the tutorial, select a specimen image from the Choose A Specimen pull-down menu and select a display image setting from the Display Image radio button collection. The Original Image option displays the unmodified specimen image and its histogram, while the Full Complement option displays the result of complementing each pixel in the specimen image. Applying the full complement operation produces an image histogram that is flipped horizontally around the center.

The Partial Complement option enables the user to adjust the complement cutoff level using the accompanying Complement Cutoff slider. As the slider is translated, the pixel brightness cutoff value is indicated on the right side of the slider and the input/output level is indicated in the Transfer Function graph. The partial complement option is useful for specimens that display extremes in contrast and brightness, but does not expose new detail for a majority of the specimens listed in the pull-down menu. As an example, the human chromosome and tissue culture cell digital images, which were both captured using fluorescence illumination, can be adjusted to reveal hidden details using the partial complement feature. Most of the other specimens in the tutorial will not benefit to a significant extent from this manipulation. Visitors should explore the effects that various settings have on each specimen image and its grayscale histogram.

The grayscale complement operation belongs to the class of image processing algorithms often referred to as point operations. These functions are utilized to transform each input pixel in an image to a modified output pixel in a manner that is dependent only on the gray level value of the input pixel. A function that is used to map input brightness values to output brightness values is known as a grayscale transformation function. In the tutorial, the Transfer Function window displays a graph of the grayscale transformation function, which transforms the specimen image to produce the complement image. A typical complement algorithm utilizes a simple relationship to compute target pixel brightness values:

Original Pixel (x,y) = 255 - Target Pixel (x,y)

Due to the logarithmic response of the human eye to illumination level differences, subtle brightness variations among the gray levels in bright regions of an image may be difficult to detect. Performing a full complement operation on a grayscale digital image reverses the brightness range of the image and produces the equivalent of a photographic negative, which can improve the visibility of gray-level variations in bright regions. In a similar manner, low-contrast images sometimes contain regions where specimen detail is obscured due to inadequate illumination. Applying a complement operation to images having poor contrast due to improper illumination can often improve the visibility of darker details by making them brighter. An effect known as solarization can be achieved by reversing only part of the input brightness range of an image. This type of complement operation is referred to as a partial complement, and can be used to improve the visibility of heavily shadowed regions in a digital image.