arXiv subjects

Lei Tian

Publications and source records attributed to Lei Tian.

At least 91 records · Page 5Linked to original sources

arXiv2019

Holographic particle localization under multiple scattering

We introduce a novel framework that incorporates multiple scattering for large-scale 3D particle-localization using single-shot in-line holography. Traditional holographic techniques rely on single-scattering models which become inaccurate under high particle-density. We demonstrate that by exploiting multiple-scattering, localization is significantly improved. Both forward and back-scattering are computed by our method under a tractable recursive framework, in which each recursion estimates the next higher-order field within the volume. The inverse scattering is presented as a nonlinear optimization that promotes sparsity, and can be implemented efficiently. We experimentally reconstruct 100 million object voxels from a single 1-megapixel hologram. Our work promises utilization of multiple scattering for versatile large-scale applications.

eess.SP↗

arXiv2019

Reliable deep-learning-based phase imaging with uncertainty quantification

Emerging deep-learning (DL)-based techniques have significant potential to revolutionize biomedical imaging. However, one outstanding challenge is the lack of reliability assessment in the DL predictions, whose errors are commonly revealed only in hindsight. Here, we propose a new Bayesian convolutional neural network (BNN)-based framework that overcomes this issue by quantifying the uncertainty of DL predictions. Foremost, we show that BNN-predicted uncertainty maps provide surrogate estimates of the true error from the network model and measurement itself. The uncertainty maps characterize imperfections often unknown in real-world applications, such as noise, model error, incomplete training data, and out-of-distribution testing data. Quantifying this uncertainty provides a per-pixel estimate of the confidence level of the DL prediction as well as the quality of the model and dataset. We demonstrate this framework in the application of large space-bandwidth product phase imaging using a physics-guided coded illumination scheme. From only five multiplexed illumination measurements, our BNN predicts gigapixel phase images in both static and dynamic biological samples with quantitative credibility assessment. Furthermore, we show that low-certainty regions can identify spatially and temporally rare biological phenomena. We believe our uncertainty learning framework is widely applicable to many DL-based biomedical imaging techniques for assessing the reliability of DL predictions.

eess.IV↗

arXiv2019

Optimal illumination scheme for isotropic quantitative differential phase contrast microscopy

Differential phase contrast microscopy (DPC) provides high-resolution quantitative phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic systems is designed with 2-axis half-circle amplitude patterns, which, however, result in a non-isotropic phase contrast transfer function (PTF). Efforts have been made to achieve isotropic DPC by replacing the conventional half-circle illumination aperture with radially asymmetric patterns with 3-axis illumination or gradient amplitude patterns with 2-axis illumination. Nevertheless, these illumination apertures were empirically designed based on empirical criteria related to the shape of the PTF, leaving the underlying theoretical mechanisms unexplored. Furthermore, the frequency responses of the PTFs under these engineered illuminations have not been fully optimized, leading to suboptimal phase contrast and signal-to-noise ratio (SNR) for phase reconstruction. In this Letter, we provide a rigorous theoretical analysis about the necessary and sufficient conditions for DPC to achieve perfectly isotropic PTF. In addition, we derive the optimal illumination scheme to maximize the frequency response for both low and high frequencies (from 0 to 2NAobj), and meanwhile achieve perfectly isotropic PTF with only 2-axis intensity measurements. We present the derivation, implementation, simulation and experimental results demonstrating the superiority of our method over state-of-the-arts in both phase reconstruction accuracy and noise-robustness.

physics.optics↗

arXiv2019

Evaluation of central corneal thickness in keratoconus and normal corneas during air puff indentation

Purpose: This study aimed to investigate the actual changes of central corneal thickness (CCT) in keratoconus and normal corneas during air puff indentation, by using corneal visualization Scheimpflug technology (Corvis ST). Methods: A total of 32 keratoconic eyes and 46 normal eyes were included in this study. Three parameters of CCTinitial, CCTfinal and CCTpeak were selected to represent the CCT at initial time, final time and highest corneal concavity, respectively, during air puff indentation. Wilcoxon signed rank test (paired sample test) was used to assess the differences between these 3 parameters in both keratoconus and normal groups. Univariate linear regression analysis was performed to determine the effect of CCTinitial on CCTpeak and CCTfinal, as well as the impact of air puff force on CCT in each group. Receiver operating characteristic (ROC) curves were constructed to evaluate the discriminative ability of the 3 parameters. Results: The results demonstrated that CCTpeak and CCTfinal were significantly decreased (p<0.01) compared to CCTinitial in both keratoconus and normal groups. Regression analysis indicated a significant positive correlation between CCTpeak and CCTinitial in normal cornea group (R2=0.337, p<0.01), but not in keratoconus group (R2=0.029, p=0.187). Likewise, regression models of air puff force and CCT revealed the different patterns of CCT changes between keratoconus and normal cornea groups. Furthermore, ROC curves showed that CCTpeak exhibited the greatest AUC (area under ROC curve) of 0.940, with accuracy, sensitivity and specificity of 94.9%, 87.5% and 100%, respectively. Conclusions: CCT may change during air puff indentation, and is significantly different between keratoconus and normal cornea groups. The changing pattern is useful for the diagnosis of keratoconus, and lays the foundation for corneal biomechanics.

physics.med-ph↗

arXiv2019

Keratoconus Recognition Using A Parameter Set Determined from IOP-Matched Scenario

PURPOSE: Among the current practices for keratoconus recognition using biomechanical parameters from Corvis ST, matching intra-ocular pressure (IOP) is often required to eliminate the biasing influence; as a result, the combined biomechanical parameters determined from IOP-unmatched scenario possibly bring in confounding influence. This paper was therefore designed to introduce a novel compatible parameter set (CPS) determined from IOP-matched scenario, hopefully could show its compatibility and superiority for recognizing keratoconus in both IOP-matched and not scenarios. METHODS: A total of 335 eyes were included. Among them, 70 eyes were used to determined CPS by forward logistics regression, 62 eyes were used to validate CPS in IOP-matched scenario, and resting 203 eyes were used to validate CPS in IOP-unmatched scenario. To analyze its superiority, CPS was also compared with other two reported Biomechanical Indexes (aCBI and DCR) in both scenarios. Receiver operating characteristic curves (ROC), accuracy, FI, sensitivity and specificity were used to access and compare the performance of these three parameter sets in both scenarios. RESULTS: The resulting CPS was comprised of only 3 biomechanical parameters: DA Ratio Max 1mm (DRM1), the first applanation time (AT1) and an energy loading parameter (Eload). In the IOP-matched validation, the area under ROC (AUC) reached 95.73%, with an accuracy of 95.2%, sensitivity of 93.5% and specificity of 96.8% (leave one out cross-validation). All these indicators reached 96.54%, 95.1%, 95.6% and 94.6% respectively, in the IOP-unmatched validation (leave one out cross-validation). Surprisingly, CPS performed better than other two parameter sets on a whole. CONCLUSIONS: The parameter set determined from IOP-matched scenario indeed exhibit its superiority for differentiation of keratoconus and normal corneas, regardless of IOP-matched or not.

physics.med-ph↗

arXiv2018

Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm

The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm. In this paper, we propose a new online PnP algorithm for Fourier ptychographic microscopy (FPM) based on the fast iterative shrinkage/threshold algorithm (FISTA). Specifically, the proposed algorithm uses only a subset of measurements, which makes it scalable to a large set of measurements. We validate the algorithm by showing that it can lead to significant performance gains on both simulated and experimental data.

cs.CV↗

arXiv2018

Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media

Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output "transmission matrix" for a fixed medium. However, this "one-to-one" mapping is highly susceptible to speckle decorrelations - small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical "one-to-all" deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.

eess.IV↗

arXiv2018

Deep learning approach to Fourier ptychographic microscopy

Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequence of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by this large spatial ensemble so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800X10800 pixels phase image using only ~25 seconds, a 50X speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ~6X. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.

cs.CV↗

arXiv2018

High-throughput intensity diffraction tomography with a computational microscope

We demonstrate a motion-free intensity diffraction tomography technique that enables direct inversion of 3D phase and absorption from intensity-only measurements for weakly scattering samples. We derive a novel linear forward model, featuring slice-wise phase and absorption transfer functions using angled illumination. This new framework facilitates flexible and efficient data acquisition, enabling arbitrary sampling of the illumination angles. The reconstruction algorithm performs 3D synthetic aperture using a robust, computation and memory efficient slice-wise deconvolution to achieve resolution up to the incoherent limit. We demonstrate our technique with thick biological samples having both sparse 3D structures and dense cell clusters. We further investigate the limitation of our technique when imaging strongly scattering samples. Imaging performance and the influence of multiple scattering is evaluated using a 3D sample consisting of stacked phase and absorption resolution targets. This computational microscopy system is directly built on a standard commercial microscope with a simple LED array source add-on, and promises broad applications by leveraging the ubiquitous microscopy platforms with minimal hardware modifications.

eess.IV↗

arXiv2017

PCANet-II: When PCANet Meets the Second Order Pooling

PCANet, as one noticeable shallow network, employs the histogram representation for feature pooling. However, there are three main problems about this kind of pooling method. First, the histogram-based pooling method binarizes the feature maps and leads to inevitable discriminative information loss. Second, it is difficult to effectively combine other visual cues into a compact representation, because the simple concatenation of various visual cues leads to feature representation inefficiency. Third, the dimensionality of histogram-based output grows exponentially with the number of feature maps used. In order to overcome these problems, we propose a novel shallow network model, named as PCANet-II. Compared with the histogram-based output, the second order pooling not only provides more discriminative information by preserving both the magnitude and sign of convolutional responses, but also dramatically reduces the size of output features. Thus we combine the second order statistical pooling method with the shallow network, i.e., PCANet. Moreover, it is easy to combine other discriminative and robust cues by using the second order pooling. So we introduce the binary feature difference encoding scheme into our PCANet-II to further improve robustness. Experiments demonstrate the effectiveness and robustness of our proposed PCANet-II method.

cs.CV↗

arXiv2017

Structured illumination microscopy with unknown patterns and a statistical prior

Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system. Generally, the reconstruction process requires prior knowledge of the illumination patterns, which implies a well-calibrated and aberration-free system. Here, we propose a new \textit{algorithmic self-calibration} strategy for SIM that does not need to know the exact patterns {\it a priori}, but only their covariance. The algorithm, termed PE-SIMS, includes a Pattern-Estimation (PE) step requiring the uniformity of the sum of the illumination patterns and a SIM reconstruction procedure using a Statistical prior (SIMS). Additionally, we perform a pixel reassignment process (SIMS-PR) to enhance the reconstruction quality. We achieve 2$\times$ better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning.

cs.CV↗

arXiv2016

Compressive Holographic Video

Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate $10\times$ temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.

cs.CV↗

arXiv2016

Indoor 5G 3GPP-like Channel Models for Office and Shopping Mall Environments

Future mobile communications systems are likely to be very different to those of today with new service innovations driven by increasing data traffic demand, increasing processing power of smart devices and new innovative applications. To meet these service demands the telecommunications industry is converging on a common set of 5G requirements which includes network speeds as high as 10 Gbps, cell edge rate greater than 100 Mbps, and latency of less than 1 msec. To reach these 5G requirements the industry is looking at new spectrum bands in the range up to 100 GHz where there is spectrum availability for wide bandwidth channels. For the development of new 5G systems to operate in bands up to 100 GHz there is a need for accurate radio propagation models which are not addressed by existing channel models developed for bands below 6 GHz. This paper presents a preliminary overview of the 5G channel models for bands up to 100 GHz in indoor offices and shopping malls, derived from extensive measurements across a multitude of bands. These studies have found some extensibility of the existing 3GPP models to the higher frequency bands up to 100 GHz. The measurements indicate that the smaller wavelengths introduce an increased sensitivity of the propagation models to the scale of the environment and show some frequency dependence of the path loss as well as increased occurrence of blockage. Further, the penetration loss is highly dependent on the material and tends to increase with frequency. The small-scale characteristics of the channel such as delay spread and angular spread and the multipath richness is somewhat similar over the frequency range, which is encouraging for extending the existing 3GPP models to the wider frequency range. Further work will be carried out to complete these models, but this paper presents the first steps for an initial basis for the model development.

cs.IT↗

arXiv2016

5G 3GPP-like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

cs.IT↗

arXiv2015

Experimental robustness of Fourier Ptychography phase retrieval algorithms

Fourier ptychography is a new computational microscopy technique that provides gigapixel-scale intensity and phase images with both wide field-of-view and high resolution. By capturing a stack of low-resolution images under different illumination angles, a nonlinear inverse algorithm can be used to computationally reconstruct the high-resolution complex field. Here, we compare and classify multiple proposed inverse algorithms in terms of experimental robustness. We find that the main sources of error are noise, aberrations and mis-calibration (i.e. model mis-match). Using simulations and experiments, we demonstrate that the choice of cost function plays a critical role, with amplitude-based cost functions performing better than intensity-based ones. The reason for this is that Fourier ptychography datasets consist of images from both brightfield and darkfield illumination, representing a large range of measured intensities. Both noise (e.g. Poisson noise) and model mis-match errors are shown to scale with intensity. Hence, algorithms that use an appropriate cost function will be more tolerant to both noise and model mis-match. Given these insights, we propose a global Newton's method algorithm which is robust and computationally efficient. Finally, we discuss the impact of procedures for algorithmic correction of aberrations and mis-calibration.

physics.optics↗

arXiv2015

Computational illumination for high-speed in vitro Fourier ptychographic microscopy

We demonstrate a new computational illumination technique that achieves large space-bandwidth-time product, for quantitative phase imaging of unstained live samples in vitro. Microscope lenses can have either large field of view (FOV) or high resolution, not both. Fourier ptychographic microscopy (FPM) is a new computational imaging technique that circumvents this limit by fusing information from multiple images taken with different illumination angles. The result is a gigapixel-scale image having both wide FOV and high resolution, i.e. large space-bandwidth product (SBP). FPM has enormous potential for revolutionizing microscopy and has already found application in digital pathology. However, it suffers from long acquisition times (on the order of minutes), limiting throughput. Faster capture times would not only improve imaging speed, but also allow studies of live samples, where motion artifacts degrade results. In contrast to fixed (e.g. pathology) slides, live samples are continuously evolving at various spatial and temporal scales. Here, we present a new source coding scheme, along with real-time hardware control, to achieve 0.8 NA resolution across a 4x FOV with sub-second capture times. We propose an improved algorithm and new initialization scheme, which allow robust phase reconstruction over long time-lapse experiments. We present the first FPM results for both growing and confluent in vitro cell cultures, capturing videos of subcellular dynamical phenomena in popular cell lines undergoing division and migration. Our method opens up FPM to applications with live samples, for observing rare events in both space and time.

physics.optics↗

arXiv2013

Hamiltonian and Phase-Space Representation of Spatial Solitons

We use Hamiltonian ray tracing and phase-space representation to describe the propagation of a single spatial soliton and soliton collisions in a Kerr nonlinear medium. Hamiltonian ray tracing is applied using the iterative nonlinear beam propagation method, which allows taking both wave effects and Kerr nonlinearity into consideration. Energy evolution within a single spatial soliton and the exchange of energy when two solitons collide are interpreted intuitively by ray trajectories and geometrical shearing of the Wigner distribution functions.

physics.optics↗

arXiv2013

Compressive X-ray phase tomography based on the transport of intensity equation

We develop and implement a compressive reconstruction method for tomographic recovery of refractive index distribution for weakly attenuating objects in a microfocus X-ray system. This is achieved through the development of a discretized operator modeling both the transport of intensity equation and X-ray transform that is suitable for iterative reconstruction techniques.

physics.optics↗