Many color-related image adjustments can be conveniently executed by exposing at most a small number of parameters to the user. Examples are tone reproduction, contrast enhancements, gamma correction, and white balancing. Others require manual touch-ups, applied by means of brush strokes. More recently, a new class of algorithms has emerged, which transfers specific image attributes from one or more example images to a target. These attributes do not have to be well-defined and concepts that are difficult to quantify with a small set of parameters, such as the “mood” of an image, can be instilled upon a target image simply through the mechanism of selecting appropriate examples. This makes example-based image manipulation a particularly suitable paradigm in creative applications, but also finds uses in more technical tasks such as stereo pair correction, video compression, image colorization, panorama stitching and creating night-time images out of day-light shots.
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