Deep Ptych: Subsampled Fourier Ptychography Using Generative Priors

Shamshad, Fahad; Abbas, Farwa; Ahmed, Ali

doi:10.1109/icassp.2019.8682179

Cited by 30 publications

(21 citation statements)

References 28 publications

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“…Alternatively, nonuniform Fourier sampling 104 and data-driven approaches can be employed to reduce the number of image acquisitions. 99,100,105 The measurement of a complex-amplitude image in Fourier ptychography enables great flexibility for postacquisition processing. For example, both the aberrations of the objective lens [Fig.…”

Section: Fourier Ptychographymentioning

confidence: 99%

Review of bio-optical imaging systems with a high space-bandwidth product

Park

Brady

Zheng³

et al. 2021

Adv. Photon.

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Optical imaging has served as a primary method to collect information about biosystems across scales-from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV). The problem originates from constituent refractive optics-the larger the aperture, the more challenging the correction of lens aberrations. Therefore, it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of millions. To address this unmet need, a variety of high-SBP imagers have emerged over the past decade, enabling an unprecedented resolution and FOV beyond the limit of conventional optics. We provide a comprehensive survey of high-SBP imaging techniques, exploring their underlying principles and applications in bioimaging.

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Section: Fourier Ptychographymentioning

confidence: 99%

Review of bio-optical imaging systems with a high space-bandwidth product

Park

Brady

Zheng³

et al. 2021

Adv. Photon.

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“…A few works have leveraged these software libraries by formulating FP phase retrieval as a gradient descent-based optimization, minimizing the error between the forward prediction and the data [179,180]. One benefit of using a cost-function-minimization-based framework is that it is straightforward to include prior information or reparameterizing the reconstruction as the output of a trained [181] or even untrained neural network [174]. Other works have altogether replaced the iterative phase retrieval algorithm with a neural network that is trained to map the raw intensity dataset to the high-resolution reconstruction, allowing for one-step reconstructions that could decrease the computation time [130,[182][183][184].…”

Section: Deep Learning In Fourier Ptychographymentioning

confidence: 99%

“…Figure courtesy of [174] techniques may be regarded as supervised learning, requiring potentially large datasets, each entry of which is a single raw FP dataset along with its high-resolution reconstruction. Some works have also demonstrated LED multiplexing or compressive sensing to reduce the number of raw measurements needed [130,181,182,184,185]. Finally, a few works have used deep learning to find the optimal illumination pattern for compressive reconstructions [131,186,187] or for application-dependent tasks [188,189].…”

Section: Deep Learning In Fourier Ptychographymentioning

confidence: 99%

Fourier ptychography: current applications and future promises

et al. 2020

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Traditional imaging systems exhibit a well-known trade-off between the resolution and the field of view of their captured images. Typical cameras and microscopes can either "zoom in" and image at high-resolution, or they can "zoom out" to see a larger area at lower resolution, but can rarely achieve both effects simultaneously. In this review, we present details about a relatively new procedure termed Fourier ptychography (FP), which addresses the above trade-off to produce gigapixel-scale images without requiring any moving parts. To accomplish this, FP captures multiple low-resolution, large field-of-view images and computationally combines them in the Fourier domain into a high-resolution, large field-of-view result. Here, we present details about the various implementations of FP and highlight its demonstrated advantages to date, such as aberration recovery, phase imaging, and 3D tomographic reconstruction, to name a few. After providing some basics about FP, we list important details for successful experimental implementation, discuss its relationship with other computational imaging techniques, and point to the latest advances in the field while highlighting persisting challenges.

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“…It is natural to introduce the idea of CNN into this inverse problem and learn an underlying mapping from the low-resolution input to a high-resolution output. [24][25][26][27] However, there exist some specific issues in biomedical applications. First, different from natural image applications, it is hard for biomedical imaging problems to access large amounts of images.…”

Section: Introductionmentioning

confidence: 99%

Neural network model assisted Fourier ptychography with Zernike aberration recovery and total variation constraint

et al. 2021

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Significance: Fourier ptychography (FP) is a computational imaging approach that achieves high-resolution reconstruction. Inspired by neural networks, many deep-learning-based methods are proposed to solve FP problems. However, the performance of FP still suffers from optical aberration, which needs to be considered. Aim:We present a neural network model for FP reconstructions that can make proper estimation toward aberration and achieve artifact-free reconstruction.Approach: Inspired by the iterative reconstruction of FP, we design a neural network model that mimics the forward imaging process of FP via TensorFlow. The sample and aberration are considered as learnable weights and optimized through back-propagation. Especially, we employ the Zernike terms instead of aberration to decrease the optimization freedom of pupil recovery and perform a high-accuracy estimation. Owing to the auto-differentiation capabilities of the neural network, we additionally utilize total variation regularization to improve the visual quality.Results: We validate the performance of the reported method via both simulation and experiment. Our method exhibits higher robustness against sophisticated optical aberrations and achieves better image quality by reducing artifacts. Conclusions:The forward neural network model can jointly recover the high-resolution sample and optical aberration in iterative FP reconstruction. We hope our method that can provide a neural-network perspective to solve iterative-based coherent or incoherent imaging problems.

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Deep Ptych: Subsampled Fourier Ptychography Using Generative Priors

Cited by 30 publications

References 28 publications

Review of bio-optical imaging systems with a high space-bandwidth product

Review of bio-optical imaging systems with a high space-bandwidth product

Fourier ptychography: current applications and future promises

Neural network model assisted Fourier ptychography with Zernike aberration recovery and total variation constraint

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