Extracting super-resolution details directly from a diffraction-blurred image or part of its frequency spectrum
- Published
- Accepted
- Subject Areas
- Computer Vision, Visual Analytics
- Keywords
- super-resolution, blurred image, diffraction-limit, details, frequency spectrum
- Copyright
- © 2019 Sheffield
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Extracting super-resolution details directly from a diffraction-blurred image or part of its frequency spectrum. PeerJ Preprints 7:e27591v2 https://doi.org/10.7287/peerj.preprints.27591v2
Abstract
It is usually believed that the low frequency part of a signal’s Fourier spectrum represents its profile, while the high frequency part represents its details. Conventional light microscopes filter the high frequency parts of image signals, so that people cannot see the details of the samples (objects being imaged) in the blurred images. However, we find that in a certain condition (isolated lighting or named separated lighting), a signal’s low frequency and high frequency parts not only represent profile and details respectively. Actually, any one of them also contains the full information (including both profile and details) of the sample’s structure. Therefore, for samples with spatial frequency beyond diffraction-limit, even if the image’s high frequency part is filtered by the microscope, it is still possible to extract the full information from the low frequency part. Based on the above findings, we propose the technique of Deconvolution Super-resolution (DeSu-re), including two methods. One method extract the full information of the sample’s structure directly from the diffraction-blurred image, while the other extract it directly from part of the observed image’s spectrum, e.g., low frequency part. Both theoretical analysis and simulation experiment support the above findings, and also verify the effectiveness of the proposed methods.
Author Comment
Three minor grammar changes have been made in the abstract.
