Reconstruction Algorithms in Undersampled AFM Imaging

Arildsen, Thomas; Oxvig, Christian Schou; Pedersen, Patrick Steffen; Østergaard, Jan; Larsen, Torben

doi:10.1109/jstsp.2015.2500363

Cited by 26 publications

(19 citation statements)

References 92 publications

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“…This review is also significant, since CS has been implemented in various research areas and applications, such as compressive imaging [24]- [26], biomedical applications [27], [28], pattern recognition [29], [30], sound processing [31]- [34], video processing [35], [36], and microelectronics applications [37], [38], and its most well-known implementation is in the field of communication systems, such as wireless networks and antenna [39]- [47]. It has been shown that this field is growing and has a wide opportunity for exploration using this technique of compression.…”

Section: Algorithm 1 Omp Algorithmmentioning

confidence: 99%

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

Nadzri

Ahmad

2022

IJEECS

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Orthogonal matching pursuit (OMP) is the most efficient algorithm used for the reconstruction of compressively sampled data signals in the implementation of compressive sensing. OMP operates in an iteration-based nature, which involves optimisation and least-square problem (LSP) as the main processes. However, optimisation and LSP processes comprise complex mathematical operations that are computationally demanding, and software-based implementations are slow, power-consuming, and unfit for real-time applications. To fill the research gap, we reviewed the optimisation and LSP techniques implemented on the FPGA platform as the hardware accelerator. Aspects that contributed to the performance, algorithm, and methods involved in the implemented works were discussed and compared. The methods were found to be improved when modified or combined. However, the best approach still depends on the requirement of the system to be developed, and this review is significant as a reference.

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Section: Algorithm 1 Omp Algorithmmentioning

confidence: 99%

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

Nadzri

Ahmad

2022

IJEECS

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“…Usually, there will be only one measurement matrix Φ and one measurement result y obtained for the AFM CS imaging because all the rows of the image are stacked together to generate a vector [19,28]. In CS AFM imaging, recovering the true sample topography from the undersampled information has high computational cost.…”

Section: Details Of Denoising Through Bayesian Compressed Sensingmentioning

confidence: 99%

“…It has been used to recover the signal from data sampled far below the classic Nyquist sampling rate under certain conditions [15–16]. Generally, the purpose of introducing CS into AFM is to increase the imaging rate [17–19]. The essentials for applying CS in AFM are the sparse representation of the image, the generation of a measurement matrix and the design of a reconstruction algorithm.…”

Section: Introductionmentioning

confidence: 99%

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

Zhang¹,

Li²,

Song³

et al. 2019

Beilstein J. Nanotechnol.

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A novel method based on Bayesian compressed sensing is proposed to remove impulse noise from atomic force microscopy (AFM) images. The image denoising problem is transformed into a compressed sensing imaging problem of the AFM. First, two different ways, including interval approach and self-comparison approach, are applied to identify the noisy pixels. An undersampled AFM image is generated by removing the noisy pixels from the image. Second, a series of measurement matrices, all of which are identity matrices with some rows removed, are constructed by recording the position of the noise-free pixels. Third, the Bayesian compressed sensing reconstruction algorithm is applied to recover the image. Different from traditional compressed sensing reconstruction methods in AFM, each row of the AFM image is reconstructed separately in the proposed method, which will not reduce the quality of the reconstructed image. The denoising experiments are conducted to demonstrate that the proposed method can remove the impulse noise from AFM images while preserving the details of the image. Compared with other methods, the proposed method is robust and its performance is not influenced by the noise density in a certain range.

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“…Compressed sensing (CS) and deep learning methods are two typical methods with excellent imaging performance [36], which use the principle of signal sparsity and the learning ability of deep neural networks to achieve super-resolution tasks. The CS has the possibility of recovering the data almost perfectly from undersampled information [37], which is widely used in AFM imaging to reduce sampling time [38][39][40][41][42][43]. The CS algorithm is mainly divided into three parts including the construction of the measurement matrix, signal sparsity, and image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Reducing molecular simulation time for AFM images based on super-resolution methods

Dou

Qian

et al. 2021

Beilstein J. Nanotechnol.

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Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.

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Reconstruction Algorithms in Undersampled AFM Imaging

Cited by 26 publications

References 92 publications

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

Reducing molecular simulation time for AFM images based on super-resolution methods

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