Just as the TDI map, the TOD is amenable to spatial super-resolution (or even sub-resolution), but in addition also to angular super-resolution.
To achieve extrapolation and related angular super-resolution, we have to select a number N > M of elements of a longer virtual ULA incorporating the actual array as described in Section 2.2.
From (45), we also find ⌊ΔN⌋ ≤ (M − 1)/2 and substituting in (44) produces frac{M-1}{2}ge leftlfloor varDelta Nrightrfloor >leftlfloor varDelta Mrightrfloor (46 confirming that to achieve angular super-resolution, it is strictly necessary that ∆ < 1/2.
To test the performance of the super-resolution reconstruction algorithm, two kinds of experiments are designed.
Note that this example of the super-resolution algorithm is performed using five low-resolution frames with ten iterations.
Fig. 8 a Schematic of a super-resolution imaging using cylindrical hyperlens [92].
The GPGPU version of the super-resolution algorithm involves writing C code with CUDA extensions.
Reconstruction-based methods are another class of image super-resolution approaches.
Learning-based methods are the third category of image super-resolution methods.
For the reconstruction of live super-resolution images the spatial and the temporal resolution are intimately correlated.
