Super-resolution imaging

In some radar and sonar imaging applications (e.g. magnetic resonance imaging (MRI), high-resolution computed tomography), subspace decomposition-based methods (e.g. MUSIC[1]) and compressed sensing-based algorithms (e.g., SAMV[2]) are employed to achieve SR over standard periodogram algorithm.

Because some of the ideas surrounding super-resolution raise fundamental issues, there is need at the outset to examine the relevant physical and information-theoretical principles: The technical achievements of enhancing the performance of imaging-forming and –sensing devices now classified as super-resolution use to the fullest but always stay within the bounds imposed by the laws of physics and information theory.

Both would use normal passband transmission but are then separately decoded to reconstitute target structure with extended resolution.

But modern technology allows probing the electromagnetic disturbance within molecular distances of the source[6] which has superior resolution properties, see also evanescent waves and the development of the new super lens.

The location of a single source can be determined by computing the "center of gravity" (centroid) of the light distribution extending over several adjacent pixels (see figure on the left).

Provided that there is enough light, this can be achieved with arbitrary precision, very much better than pixel width of the detecting apparatus and the resolution limit for the decision of whether the source is single or double.

Then conclusions can be drawn, using statistical methods, from the available image data about the presence of the full object.

This can be achieved at separations well below the classical resolution bounds, and requires the prior limitation to the choice "single or double?"

[14] More recently, a fast single image super-resolution algorithm based on a closed-form solution to

problems has been proposed and demonstrated to accelerate most of the existing Bayesian super-resolution methods significantly.

[15] Geometrical SR reconstruction algorithms are possible if and only if the input low resolution images have been under-sampled and therefore contain aliasing.