Reconstruction of undersampled atomic force microscope images using block-based compressive sensing

Han, Guoqiang; Niu, Yixiang; Zou, Yu; Lin, Bo

doi:10.1016/j.apsusc.2019.04.157

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Accordingly, we present two methods for reconstructing sparse PFM spiral scans: 1) compressive sensing [ 54–60 ] (CS) and 2) Gaussian process [ 61–65 ] (GP) regression. The compressive sensing image inpainting algorithm requires two inputs: 1) The sparse and noisy measurements y , and 2) the scanning mask indicating sampled pixel locations.…”

Section: Introductionmentioning

confidence: 99%

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Kelley

Ziatdinov

Collins

et al. 2020

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Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. This study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

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Section: Introductionmentioning

confidence: 99%

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Kelley

Ziatdinov

Collins

et al. 2020

Small

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“…This approach has been valuable in applications such as magnetic resonance imaging in medical imaging to achieve the contribution of shorter scanning time and wireless communications to facilitate efficient signal transmissions in bandwidth-limited environments. 10) The CS approach demonstrated that AFM could achieve faster speeds of imaging sample topography without compromising image quality, which is critical for observing dynamic processes or reducing sample damage, thereby broadening the potential for real-time applications for reconstructing the topographic images of solid samples, 11) polymer patterned samples, 12,13) biological molecules, 13) and single cells. 14,15) However, how CS is effective in reconstructing mechanical images of biological samples obtained by AFM remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing reconstruction of cell mechanical images obtained from atomic force microscopy

Miyata,

Chan,

Uchihashi

et al. 2024

Jpn. J. Appl. Phys.

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Compressed sensing (CS), a technique in signal processing that reconstructs sparse signals from a limited sampling number, has been valuable in topographic images obtained from atomic force microscopy (AFM). However, how CS is effective in reconstructing AFM mechanical images remains unclear. We investigated the reconstruction of topographic and mechanical images of living cells, such as developing embryos obtained from AFM mapping experiments using CS. The results showed that both topographic and mechanical images of embryonic cells in the different developmental stages were well reconstructed at a spatial resolution higher than the original AFM images. These results suggested that the CS approach enabled the cell mechanical properties, together with cell surface morphology, using AFM mapping measurements to be faster than the conventional AFM methods without reducing the spatial resolution.

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“…If the imaging process is too long, the compressive imaging method can be applied [6]. If the measurements are undersampled and must be done too quickly, reconstruction of data can be used [7].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Method for Submolecular Resolution of Helicene-Based Macrocycles by Atomic Force Microscopy in Air

Ukraintsev¹,

Houska²,

Vacek³

et al. 2020

NANOCON 2019 Conference Proeedings

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We introduce a straightforward mathematical method for improving the AFM image resolution, applied to image analysis of helicene-based macrocycles adsorbed on HOPG. The method reveals structural details from insufficiently resolved AFM images and attributes them to internal structure and ordering of the macrocycles. Our findings are also corroborated by molecular mechanics simulations, validating that the structure provided by the method has lower potential energy compared to other tested macrocycle arrangements.

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Reconstruction of undersampled atomic force microscope images using block-based compressive sensing

Cited by 6 publications

References 36 publications

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Compressed sensing reconstruction of cell mechanical images obtained from atomic force microscopy

Mathematical Method for Submolecular Resolution of Helicene-Based Macrocycles by Atomic Force Microscopy in Air

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