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Fast and simple super-resolution with single images.

ABSTRACT: We present a fast and simple algorithm for super-resolution with single images. It is based on penalized least squares regression and exploits the tensor structure of two-dimensional convolution. A ridge penalty and a difference penalty are combined; the former removes singularities, while the latter eliminates ringing. We exploit the conjugate gradient algorithm to avoid explicit matrix inversion. Large images are handled with ease: zooming a 100 by 100 pixel image to 800 by 800 pixels takes less than a second on an average PC. Several examples, from applications in wide-field fluorescence microscopy, illustrate performance.

SUBMITTER: Eilers PHC

PROVIDER: S-EPMC9253020 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Fast and simple super-resolution with single images.

Eilers Paul H C PHC Ruckebusch Cyril C

Scientific reports 20220704 1

We present a fast and simple algorithm for super-resolution with single images. It is based on penalized least squares regression and exploits the tensor structure of two-dimensional convolution. A ridge penalty and a difference penalty are combined; the former removes singularities, while the latter eliminates ringing. We exploit the conjugate gradient algorithm to avoid explicit matrix inversion. Large images are handled with ease: zooming a 100 by 100 pixel image to 800 by 800 pixels takes le ...[more]

PMID: 35787655

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