Multiscale Super Resolution without Image Priors
Daniel Fu, Gabby Litterio, Pedro Felzenszwalb, Rashid Zia

TL;DR
This paper proposes a multiscale super-resolution method that combines images at different scales to resolve ambiguities, using Fourier and iterative techniques validated through experiments with CCD hardware binning.
Contribution
It introduces a multiscale super-resolution approach leveraging scale differences and coprime pixel sizes, with mathematical analysis and practical validation.
Findings
Stable inverse systems with coprime pixel sizes enable super-resolution reconstruction.
Fourier domain and least squares methods efficiently reconstruct high-resolution images.
Experimental validation demonstrates improved resolution over a range of pixel sizes.
Abstract
We address the ambiguities in the super-resolution problem under translation. We demonstrate that combinations of low-resolution images at different scales can be used to make the super-resolution problem well posed. Such differences in scale can be achieved using sensors with different pixel sizes (as demonstrated here) or by varying the effective pixel size through changes in optical magnification (e.g., using a zoom lens). We show that images acquired with pairwise coprime pixel sizes lead to a system with a stable inverse, and furthermore, that super-resolution images can be reconstructed efficiently using Fourier domain techniques or iterative least squares methods. Our mathematical analysis provides an expression for the expected error of the least squares reconstruction for large signals assuming i.i.d. noise that elucidates the noise-resolution tradeoff. These results are…
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