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Digital images begin with a fixed number of pixels in a two-dimensional grid. AI super resolution uses machine learning (ML) algorithms to infer from an original image ways that more pixels may be added to improve that image in some way. Fundamentally, the technology increases the resolution by creating a version of the image with more pixels that can offer greater detail. The algorithms generate the best colors to use for the interpolated pixels.
How is AI super resolution used?
Super resolution algorithms are commonly used to improve the display of images and video. Many televisions, for instance, may be able to display a grid of 3840 x 2160 pixels, sometimes called 4K (an approximation of the horizontal number of pixels) or ultra high definition (UHD). Many TV signals, however, are broadcast only with grids of 1920 x 1080 pixels, also known as 1080p. AI algorithms convert each pixel in the 1080p signal into a grid of four pixels, effectively creating information and making the image quality more detailed.
Super resolution algorithms are also being deployed with digital cameras and medical instrumentation. The algorithms provide higher resolutions that can be essential for engineering, construction, surgery and other practices that rely upon cameras to gather important details.
How does AI super resolution work?
The visual output of super resolution, sometimes called “upsampling,” varies depending upon the algorithm. The simplest solution is to not to try to infer any new detail and simply replace each pixel with four identical pixels of the same color. This may create a larger grid, but there is no more detail.