Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images

Bidragets oversatte titel: Struktur-assisteret compressed sensing rekonstruktion af under-samplede AFM-billeder

Christian Schou Oxvig, Thomas Arildsen, Torben Larsen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

10 Citationer (Scopus)

Resumé

The use of compressed sensing in atomic force microscopy (AFM) can potentially speed-up image acquisition, lower probe-specimen interaction, or enable super resolution imaging. The idea in compressed sensing for AFM is to spatially undersample the specimen, i.e. only acquire a small fraction of the full image of it, and then use advanced computational techniques to reconstruct the remaining part of the image whenever this is possible. Our initial experiments have shown that it is possible to leverage inherent structure in acquired AFM images to improve image reconstruction. Thus, we have studied structure in the discrete cosine transform coefficients of typical AFM images. Based on this study, we propose a generic support structure model that may be used to improve the quality of the reconstructed AFM images. Furthermore, we propose a modification to the established iterative thresholding reconstruction algorithms that enables the use of our proposed structure model in the reconstruction process. Through a large set of reconstructions, the general reconstruction capability improvement achievable using our structured model is shown both quantitatively and qualitatively. Specifically, our experiments show that our proposed algorithm improves over established iterative thresholding algorithms by being able to reconstruct AFM images to a comparable quality using fewer measurements or equivalently obtaining a more detailed reconstruction for a fixed number of measurements.
OriginalsprogEngelsk
TidsskriftUltramicroscopy
Vol/bind172
Sider (fra-til)1-9
ISSN0304-3991
DOI
StatusUdgivet - jan. 2017

Fingerprint

Compressed sensing
Atomic force microscopy
atomic force microscopy
Model structures
Discrete cosine transforms
Image acquisition
discrete cosine transform
Image reconstruction
image reconstruction
Experiments
acquisition
Imaging techniques
probes
coefficients

Citer dette

@article{14490a347d3340ccbfcd18f379aac101,
title = "Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images",
abstract = "The use of compressed sensing in atomic force microscopy (AFM) can potentially speed-up image acquisition, lower probe-specimen interaction, or enable super resolution imaging. The idea in compressed sensing for AFM is to spatially undersample the specimen, i.e. only acquire a small fraction of the full image of it, and then use advanced computational techniques to reconstruct the remaining part of the image whenever this is possible. Our initial experiments have shown that it is possible to leverage inherent structure in acquired AFM images to improve image reconstruction. Thus, we have studied structure in the discrete cosine transform coefficients of typical AFM images. Based on this study, we propose a generic support structure model that may be used to improve the quality of the reconstructed AFM images. Furthermore, we propose a modification to the established iterative thresholding reconstruction algorithms that enables the use of our proposed structure model in the reconstruction process. Through a large set of reconstructions, the general reconstruction capability improvement achievable using our structured model is shown both quantitatively and qualitatively. Specifically, our experiments show that our proposed algorithm improves over established iterative thresholding algorithms by being able to reconstruct AFM images to a comparable quality using fewer measurements or equivalently obtaining a more detailed reconstruction for a fixed number of measurements.",
keywords = "atomic force microscopy (AFM), compressed sensing, compressive sampling, undersampling, Image Reconstruction, sparsity modelling",
author = "Oxvig, {Christian Schou} and Thomas Arildsen and Torben Larsen",
year = "2017",
month = "1",
doi = "10.1016/j.ultramic.2016.09.011",
language = "English",
volume = "172",
pages = "1--9",
journal = "Ultramicroscopy",
issn = "0304-3991",
publisher = "Elsevier",

}

Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images. / Oxvig, Christian Schou; Arildsen, Thomas; Larsen, Torben.

I: Ultramicroscopy, Bind 172, 01.2017, s. 1-9.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images

AU - Oxvig, Christian Schou

AU - Arildsen, Thomas

AU - Larsen, Torben

PY - 2017/1

Y1 - 2017/1

N2 - The use of compressed sensing in atomic force microscopy (AFM) can potentially speed-up image acquisition, lower probe-specimen interaction, or enable super resolution imaging. The idea in compressed sensing for AFM is to spatially undersample the specimen, i.e. only acquire a small fraction of the full image of it, and then use advanced computational techniques to reconstruct the remaining part of the image whenever this is possible. Our initial experiments have shown that it is possible to leverage inherent structure in acquired AFM images to improve image reconstruction. Thus, we have studied structure in the discrete cosine transform coefficients of typical AFM images. Based on this study, we propose a generic support structure model that may be used to improve the quality of the reconstructed AFM images. Furthermore, we propose a modification to the established iterative thresholding reconstruction algorithms that enables the use of our proposed structure model in the reconstruction process. Through a large set of reconstructions, the general reconstruction capability improvement achievable using our structured model is shown both quantitatively and qualitatively. Specifically, our experiments show that our proposed algorithm improves over established iterative thresholding algorithms by being able to reconstruct AFM images to a comparable quality using fewer measurements or equivalently obtaining a more detailed reconstruction for a fixed number of measurements.

AB - The use of compressed sensing in atomic force microscopy (AFM) can potentially speed-up image acquisition, lower probe-specimen interaction, or enable super resolution imaging. The idea in compressed sensing for AFM is to spatially undersample the specimen, i.e. only acquire a small fraction of the full image of it, and then use advanced computational techniques to reconstruct the remaining part of the image whenever this is possible. Our initial experiments have shown that it is possible to leverage inherent structure in acquired AFM images to improve image reconstruction. Thus, we have studied structure in the discrete cosine transform coefficients of typical AFM images. Based on this study, we propose a generic support structure model that may be used to improve the quality of the reconstructed AFM images. Furthermore, we propose a modification to the established iterative thresholding reconstruction algorithms that enables the use of our proposed structure model in the reconstruction process. Through a large set of reconstructions, the general reconstruction capability improvement achievable using our structured model is shown both quantitatively and qualitatively. Specifically, our experiments show that our proposed algorithm improves over established iterative thresholding algorithms by being able to reconstruct AFM images to a comparable quality using fewer measurements or equivalently obtaining a more detailed reconstruction for a fixed number of measurements.

KW - atomic force microscopy (AFM)

KW - compressed sensing

KW - compressive sampling

KW - undersampling

KW - Image Reconstruction

KW - sparsity modelling

UR - https://doi.org/10.5281/zenodo.60512

U2 - 10.1016/j.ultramic.2016.09.011

DO - 10.1016/j.ultramic.2016.09.011

M3 - Journal article

VL - 172

SP - 1

EP - 9

JO - Ultramicroscopy

JF - Ultramicroscopy

SN - 0304-3991

ER -