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Local contrast stretching considers each range of color palate in the image (R, G, and B) separately, providing a set of minimum and maximum values for each color palate. Global Contrast Stretching, on the other hand, considers all color palate ranges at once to determine the maximum and minimum values for the entire RGB color image.
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.
TEM Ray Diagram with Phase Contrast Transfer Function. Contrast transfer theory provides a quantitative method to translate the exit wavefunction to a final image. Part of the analysis is based on Fourier transforms of the electron beam wavefunction. When an electron wavefunction passes through a lens, the wavefunction goes through a Fourier ...
The advantages of these methods compared to normal absorption-contrast X-ray imaging is higher contrast for low-absorbing materials (because phase shift is a different mechanism than absorption) and a contrast-to-noise relationship that increases with spatial frequency (because many phase-contrast techniques detect the first or second ...
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 ...
Contrary to conventional phase contrast images [citation needed], phase shift images of living cells are suitable to be processed by image analysis software. This has led to the development of non-invasive live cell imaging and automated cell culture analysis systems based on quantitative phase contrast microscopy.
The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009.