Histogram image processing is the act of modifying an image by controlling the parameters of the image intensity values. Intensity values for an image can be measured and mapped onto a histogram, representing either overall intensity or color intensity within a single color channel. A user can change the appearance of a picture through histogram image processing by interacting with and modifying that histogram displaying intensity values.
A color's intensity throughout an image can be calculated, and the calculated values can be mapped onto a histogram. By changing the shape of that histogram or altering the midpoint of it, intensity values can be remapped, changing the appearance of an image. It's possible to modify a histogram for a single color channel, changing how a single color is treated throughout an image. Along with intensification or attenuation of selected colors, overall contrast within an image can also be increased or decreased.
Many image processing applications have the capacity to allow the user to view the histogram associated with an image's intensity values. Image processing technology allows a user who has basic knowledge of image editing to change the appearance of an image by overall intensity values without having to apply modifications directly to the image itself. Histogram image processing is especially useful when a person wants to combine two images for either a normalized comparison or to stitch images together seamlessly. By normalizing an image histogram relative to other image histograms, each image gains the same range of color and intensity as the other images.
Image visualization is important in many fields of study, from art and photography to scientific data collection. Histogram image processing can create a virtual image filter, creating artistic elements or reducing unwanted imaging effects. Edge detection and image segmentation can be made more apparent, increasing the likelihood for an automated program to be able to identify elements within a single image. This is especially useful in collecting scientific data from images.
For scientific purposes, histogram image processing is critical because it eradicates variables in images caused by a systematic shift. All images in a study can be given the same parameter range and normalized to one another, allowing scientists to control for variables caused by changes in imaging parameters. By controlling for image variance using histogram image processing, scientists are then able to examine the variables within a study without confounding degrees of image intensity or contrast. In other words, by using image processing algorithms to normalize intensity values within images, a scientist can directly compare otherwise incomparable images.
