Fast and simple super-resolution with single images

Paul H.C. Eilers, Cyril Ruckebusch*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
18 Downloads (Pure)


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.

Original languageEnglish
Article number11241
JournalScientific Reports
Issue number1
Publication statusPublished - 4 Jul 2022

Bibliographical note

Publisher Copyright: © 2022, The Author(s).


Dive into the research topics of 'Fast and simple super-resolution with single images'. Together they form a unique fingerprint.

Cite this