78 research outputs found
System calibration method for Fourier ptychographic microscopy
Fourier ptychographic microscopy (FPM) is a recently proposed quantitative
phase imaging technique with high resolution and wide field-of-view (FOV). In
current FPM imaging platforms, systematic error sources come from the
aberrations, LED intensity fluctuation, parameter imperfections and noise,
which will severely corrupt the reconstruction results with artifacts. Although
these problems have been researched and some special methods have been proposed
respectively, there is no method to solve all of them. However, the systematic
error is a mixture of various sources in the real situation. It is difficult to
distinguish a kind of error source from another due to the similar artifacts.
To this end, we report a system calibration procedure, termed SC-FPM, based on
the simulated annealing (SA) algorithm, LED intensity correction and adaptive
step-size strategy, which involves the evaluation of an error matric at each
iteration step, followed by the re-estimation of accurate parameters. The great
performance has been achieved both in simulation and experiments. The reported
system calibration scheme improves the robustness of FPM and relaxes the
experiment conditions, which makes the FPM more pragmatic.Comment: 18 pages, 9 figure
Motion-corrected Fourier ptychography
Fourier ptychography (FP) is a recently proposed computational imaging
technique for high space-bandwidth product imaging. In real setups such as
endoscope and transmission electron microscope, the common sample motion
largely degrades the FP reconstruction and limits its practicability. In this
paper, we propose a novel FP reconstruction method to efficiently correct for
unknown sample motion. Specifically, we adaptively update the sample's Fourier
spectrum from low spatial-frequency regions towards high spatial-frequency
ones, with an additional motion recovery and phase-offset compensation
procedure for each sub-spectrum. Benefiting from the phase retrieval redundancy
theory, the required large overlap between adjacent sub-spectra offers an
accurate guide for successful motion recovery. Experimental results on both
simulated data and real captured data show that the proposed method can correct
for unknown sample motion with its standard deviation being up to 10% of the
field-of-view scale. We have released our source code for non-commercial use,
and it may find wide applications in related FP platforms such as endoscopy and
transmission electron microscopy
Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging
We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We show that this illumination coding scheme is highly scalable in achieving flexible resolution, and robust to experimental variations. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5X resolution enhancement across 4X FOVs using only five multiplexed measurements -- more than 10X data reduction over the state-of-the-art. Typical DL algorithms tend to provide over-confident predictions, whose errors are only discovered in hindsight. We develop an uncertainty learning framework to overcome this limitation and provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and "out-of-distribution" testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable AI-augmented large-SBP phase imaging with dependable predictions.Published versio
SNR-based adaptive acquisition method for fast Fourier ptychographic microscopy
Fourier ptychographic microscopy (FPM) is a computational imaging technique
with both high resolution and large field-of-view. However, the effective
numerical aperture (NA) achievable with a typical LED panel is ambiguous and
usually relies on the repeated tests of different illumination NAs. The imaging
quality of each raw image usually depends on the visual assessments, which is
subjective and inaccurate especially for those dark field images. Moreover, the
acquisition process is really time-consuming.In this paper, we propose a
SNR-based adaptive acquisition method for quantitative evaluation and adaptive
collection of each raw image according to the signal-to-noise ration (SNR)
value, to improve the FPM's acquisition efficiency and automatically obtain the
maximum achievable NA, reducing the time of collection, storage and subsequent
calculation. The widely used EPRY-FPM algorithm is applied without adding any
algorithm complexity and computational burden. The performance has been
demonstrated in both USAF targets and biological samples with different imaging
sensors respectively, which have either Poisson or Gaussian noises model.
Further combined with the sparse LEDs strategy, the number of collection images
can be shorten to around 25 frames while the former needs 361 images, the
reduction ratio can reach over 90%. This method will make FPM more practical
and automatic, and can also be used in different configurations of FPM.Comment: 11 pages, 6 figure
Edge effect removal in Fourier ptychographic microscopy via perfect Fourier transformation (PFT)
Edge effect may degrade the imaging precision and is caused by the aperiodic
image extension of fast Fourier transform (FFT). In this letter, a perfect
Fourier transform algorithm termed PFT was reported to remove the artifacts
with comparable efficiency to FFT. Although we demonstrated the performance of
PFT in Fourier ptychographic microscopy (FPM) only, it can be expanded in any
occasion where the conventional FFT is used.Comment: 4 pages, 6 figure
Fourier ptychography: current applications and future promises
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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