Search results
Results from the WOW.Com Content Network
A Contrast Stretching Transformation can be achieved by: Contrast Stretching Transformation Graph reference for derivation. 1. Stretching the dark range of input values into a wider range of output values: This involves increasing the brightness of the darker areas in the image to enhance details and improve visibility. 2.
Image enhancement techniques (like contrast stretching or de-blurring by a nearest neighbor procedure) provided by imaging packages use no a priori model of the process that created the image. With image enhancement noise can effectively be removed by sacrificing some resolution, but this is not acceptable in many applications.
Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.
Often the contrast reduction is of most interest and the translation of the pattern can be ignored. The relative contrast is given by the absolute value of the optical transfer function, a function commonly referred to as the modulation transfer function (MTF). Its values indicate how much of the object's contrast is captured in the image as a ...
You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.
The maximum contrast of an image is termed the contrast ratio or dynamic range. In images where the contrast ratio approaches the maximum possible for the medium, there is a conservation of contrast. In such cases, increasing contrast in certain parts of the image will necessarily result in a decrease in contrast elsewhere.
Phase stretch transform (PST) is a computational approach to signal and image processing. One of its utilities is for feature detection and classification. One of its utilities is for feature detection and classification.
For example, if an algorithm is looking for a face, its template eigenspaces may consist of images (i.e., templates) of faces in different positions to the camera, in different lighting conditions, or with different expressions (i.e., poses). It is also possible for a matching image to be obscured or occluded by an object.