Adaptive Ptych: Leveraging Image Adaptive Generative Priors for Subsampled Fourier Ptychography

Shamshad, Fahad; Hanif, Asif; Abbas, Farwa; Awais, Muhammad; Ahmed, Ali

doi:10.1109/iccvw.2019.00476

Cited by 5 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Deep Learning In Fourier Ptychographymentioning

confidence: 99%

Fourier ptychography: current applications and future promises

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In [9], authors show the effectiveness of pre-trained generative models for handling the problem of blind image deblurring. Recently, pre-trained generative models have also shown remarkable performance for solving other inverse imaging problems including compressed sensing [16], Fourier ptychography [26,17], phase retrieval [18] etc. These pre-trained generative priors bridge the gap between deep learning based approaches (that can take advantage of the powerful learned priors) and conventional hand designed priors such as sparsity (that are flexible enough to handle variety of model parameters).…”

Section: Algorithm 1 Phaseless Deblurring Under Generative Priorsmentioning

confidence: 99%

Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior

Shamshad¹,

Ahmed²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we consider the highly ill-posed problem of jointly recovering two real-valued signals from the phaseless measurements of their circular convolution. The problem arises in various imaging modalities such as Fourier ptychography, X-ray crystallography, and in visible light communication. We propose to solve this inverse problem using alternating gradient descent algorithm under two pretrained deep generative networks as priors; one is trained on sharp images and the other on blur kernels. The proposed recovery algorithm strives to find a sharp image and a blur kernel in the range of the respective pre-generators that best explain the forward measurement model. In doing so, we are able to reconstruct quality image estimates. Moreover, the numerics show that the proposed approach performs well on the challenging measurement models that reflect the physically realizable imaging systems and is also robust to noise.

show abstract

“…In the context of FP, deep neural networks have been trained in a supervised manner as nonlinear mappings that take the low-resolution measurements and output the high-resolution image of interest [32][33][34]. Further, in [35,36], pre-trained deep generative priors are used to solve the PR problem. For more details regarding FP, we refer the reader to recent comprehensive reviews [20,37].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Fourier ptychography with deep spatiotemporal priors

et al. 2023

View full text Add to dashboard Cite

Fourier ptychography (FP) involves the acquisition of several low-resolution intensity images of a sample under varying illumination angles. They are then combined into a high-resolution complex-valued image by solving a phase-retrieval problem. The objective in dynamic FP is to obtain a sequence of high-resolution images of a moving sample. There, the application of standard frame-by-frame reconstruction methods limits the temporal resolution due to the large number of measurements that must be acquired for each frame. In this work instead, we propose a neural-network-based reconstruction framework for dynamic FP. Specifically, each reconstructed image in the sequence is the output of a shared deep convolutional network fed with an input vector that lies on a one-dimensional manifold that encodes time. We then optimize the parameters of the network to fit the acquired measurements. The architecture of the network and the constraints on the input vectors impose a spatiotemporal regularization on the sequence of images. This enables our method to achieve high temporal resolution without compromising the spatial resolution. The proposed framework does not require training data. It also recovers the pupil function of the microscope. Through numerical experiments, we show that our framework paves the way for high-quality ultrafast FP.

show abstract

Adaptive Ptych: Leveraging Image Adaptive Generative Priors for Subsampled Fourier Ptychography

Cited by 5 publications

References 20 publications

Fourier ptychography: current applications and future promises

Fourier ptychography: current applications and future promises

Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior

Dynamic Fourier ptychography with deep spatiotemporal priors

Contact Info

Product

Resources

About