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An example of super-resolution processing using PhotoAcute software. The left half shows the original, blown up to twice its normal size. The right half shows the image (at native resolution) that results when the software combines nine images together and performs 2x superresolution.

Super-resolution (SR) are techniques that in some way enhance the resolution of an imaging system. There are different views as to what is considered an SR-technique: some consider only techniques that break the diffraction-limit of systems, while others also consider techniques that merely break the limit of the digital imaging sensor as SR.

There are both single-frame and multiple-frame variants of SR, where multiple-frame are the most useful. Algorithms can also be divided by their domain: frequency or space domain. By fusing together several low-resolution (LR) one enhanced-resolution image is formed.

The necessity of aliasing

In the most common SR algorithms, the information that was gained in the SR-image was embedded in the LR images in the form of aliasing. This requires that the capturing sensor in the system is weak enough so that aliasing is actually happening. A diffraction-limited system contains no aliasing, for example, or a system where the total system Modulation Transfer Function is filtering out high-frequency content.

Breaking the diffraction limit

There are also SR techniques that extrapolate the image in the frequency domain, by assuming that the object on the image is an analytic function, and that we can exactly know the function values in some interval. This method is severely limited by the noise that is ever-present in digital imaging systems, but it can work for radar, astronomy or microscopy.