Histogram matching converts the histogram, the profile of light and dark elements, in a target image to that of a reference image. It is used in image processing to adjust one or more images for better clarity, quality, or other traits. Computer programs can perform this procedure automatically when directed to do so by the user. Complex algorithms are involved to ensure that the data remains as intact as possible.
In a histogram, important information about the distribution of light and dark in an image is provided, classically in the form of a bar graph. Each bar in the graph indicates the number of pixels that share a specific trait, from absolute darkness to complete whiteness. Histograms can be used with both color and black and white images. In matching procedures, images are run through an algorithm to line their histograms up.
One common application of this technology is in the processing of satellite images. Researchers may want to make a mosaic of images that were taken at different times, angles, or in different conditions. For example, some consumers use mapping programs and expect to be able to see a satellite view of an area which may include an assortment of images taken by different cameras and at different times. In histogram matching, the color and tone profiles of the images are adjusted so they can be matched up with a minimum of clashing.
This procedure is also used for handling medical images, restoration of photographs, or processing of data from sensors that are not always completely reliable. The reference image provides a baseline histogram for a computer algorithm to use in the match. In the process, it is important to be aware that gaps and errors can be introduced to the image. Missing data can create problems, depending on the image and the level of adjustment that needs to take place, and may be a cause for concern in some settings. Computer programs used in histogram matching can be adjusted to address this.
As with other digital image adjustments, it is possible to reverse histogram matching if it does not work as expected. The user can ask the computer to undo the image transformation and return the target to its previous state. This can allow the user to determine what went wrong, and adjust the parameters of the histogram matching for a more successful second attempt. If an image is particularly sensitive, it may be advisable to leave the raw file intact and make a copy for processing and transformation, in case of accidents.