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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.
Producing a diffraction-free image was ultimately solved through the projection aligner systems, which dominated chip making through the 1970s and early 1980s. The relentless drive of Moore's law ultimately reached the limit of what the projection aligners could handle. Efforts were made to extend their lifetimes by moving to ever-higher UV ...
Super-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced.
STED is a deterministic functional technique that exploits the non-linear response of fluorophores commonly used to label biological samples in order to achieve an improvement in resolution, that is to say STED allows for images to be taken at resolutions below the diffraction limit.
An image scaled with nearest-neighbor scaling (left) and 2×SaI scaling (right) In computer graphics and digital imaging, image scaling refers to the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement.
For example, if applied to 8-bit image displayed with 8-bit gray-scale palette it will further reduce color depth (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images.
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