Enhancement of Image Detail
The procedures described and illustrated above are all intended to compensate for various limitations and defects that arise in acquiring a digitized image. Their goal is to produce a correct representation of the original scene. By proper use of many of the same tools, it is also possible to enhance the visibility of some details and information in the image. This is accomplished by removing or suppressing other information (which is not currently of interest), so that what remains is more readily seen by human vision, and/or more readily isolated for measurement.
Increasing Local Contrast - The previous section showed adjustments to the brightness and contrast of the entire image. The adjustment changes every pixel of original brightness value A to the same final brightness value B, regardless of the pixel’s neighbors. Another class of operations increases the visibility of local differences between pixels, by suppressing the longer-range variations. These neighborhood functions use a moving neighborhood, usually a small circle, that compares or combines the central pixel and the neighbors to produce a new value that is assigned to the central pixel to construct a new image. Then the neighborhood shifts to the next pixel and the process is repeated. These calculations are applied to the pixel brightness values in a color coordinate system such as HSI or LAB that leaves the color values unchanged.
Pseudocolor and Rendering - Substituting a palette of colors for the brightness values of a monochrome image produces a false-color or pseudo-color result that in some cases makes it easier to see small changes in brightness, or to compare the brightness of features. Because most people can see color, and can distinguish hundreds of colors, vs. only tens of gray scale brightness levels, this seems like an appealing idea and is very widely used. Human vision is also highly adapted to view surfaces, and rendering the brightness values as elevation can also reveal details.
Steps and Edges - The boundaries of features and structural details in an image are usually defined by changes in brightness or color. Locating these edges so that they can be accurately measured has been an active area of development in image processing. In addition, there is considerable evidence that the steps and edges are key components of a scene selected by human visual processes, and that either extracting them from an image (forming a “sketch” of the scene) or sharpening their appearance assists viewers.
Texture and Directionality - In many cases, the objects or structures that need to be distinguished in images are not characterized by color or brightness values that are different from the surrounding background. Instead, the difference may be one of texture. Human vision recognizes features based on this property, and software that converts textural differences to brightness differences can help to delineate structures.
Cross-Correlation - Fourier-space processing has been shown above to offer a powerful way to remove periodic noise from images. It is also used to perform convolutions, such as Gaussian smoothing or high-pass sharpening filters, more efficiently than can be done with large kernels applied directly to the pixel values. Deconvolution in which out-of-focus images are restored was also shown above. Another very useful Fourier-space technique is cross-correlation to locate features within an image.
John C. Russ - Materials Science and Engineering Dept., North Carolina State University, Raleigh, North Carolina, 27695.
Matthew Parry-Hill and Michael W. Davidson - National High Magnetic Field Laboratory, 1800 East Paul Dirac Dr., The Florida State University, Tallahassee, Florida, 32310.
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