Struktur-assisteret compressed sensing rekonstruktion af under-samplede AFM-billeder

Christian Schou Oxvig, Thomas Arildsen, Torben Larsen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

9 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.
Bidragets oversatte titelStruktur-assisteret compressed sensing rekonstruktion af under-samplede AFM-billeder
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

Emneord

    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

    UR - http://dx.doi.org/10.5278/vbn/projects/wISTwIHT

    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 -