Visualization 1: Multilayer fluorescence imaging on a single-pixel detector

Visualization 1 Originally published in Biomedical Optics Express on 01 July 2016 (boe-7-7-2425

Visualization 2: Multilayer fluorescence imaging on a single-pixel detector

Visualization 2 Originally published in Biomedical Optics Express on 01 July 2016 (boe-7-7-2425

Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow

Fourier ptychography is a recently developed imaging approach for large field-of-view and high-resolution microscopy. Here we model the Fourier ptychographic forward imaging process using a convolution neural network (CNN) and recover the complex object information in the network training process. In this approach, the input of the network is the point spread function in the spatial domain or the coherent transfer function in the Fourier domain. The object is treated as 2D learnable weights of a convolution or a multiplication layer. The output of the network is modeled as the loss function we aim to minimize. The batch size of the network corresponds to the number of captured low-resolution images in one forward / backward pass. We use a popular open-source machine learning library, TensorFlow, for setting up the network and conducting the optimization process. We analyze the performance of different learning rates, different solvers, and different batch sizes. It is shown that a large batch size with the Adam optimizer achieves the best performance in general. To accelerate the phase retrieval process, we also discuss a strategy to implement Fourier-magnitude projection using a multiplication neural network model. Since convolution and multiplication are the two most-common operations in imaging modeling, the reported approach may provide a new perspective to examine many coherent and incoherent systems. As a demonstration, we discuss the extensions of the reported networks for modeling single-pixel imaging and structured illumination microscopy (SIM). 4-frame resolution doubling is demonstrated using a neural network for SIM. We have made our implementation code open-source for the broad research community

Visualization 2: Recovering higher dimensional image data using multiplexed structured illumination

Visualization 2 Originally published in Optics Express on 16 November 2015 (oe-23-23-30393

Visualization 1: Recovering higher dimensional image data using multiplexed structured illumination

Visualization 1 Originally published in Optics Express on 16 November 2015 (oe-23-23-30393

Supplementary document for Deep distributed optimization for blind diffuser-modulation ptychography - 5843007.pdf

supplementary materia

Pixel Super-Resolved Lensless on-Chip Sensor with Scattering Multiplexing

Lensless on-chip microscopy has shown great potential for biomedical imaging due to its large-area and high-throughput imaging capabilities. By combining the pixel super-resolution (PSR) technique, it can improve the resolution beyond the limit of the imaging detector. However, existing PSR techniques are restricted by the feature size and crosstalk of modulation components (such as spatial light modulator), which cannot efficiently encode target information. Besides, the reconstruction algorithms suffer from the trade-off between reconstruction quality, imaging resolution, and computational efficiency. In this work, we constructed a novel integrated lensless on-chip sensor via scattering multiplexing and reported a robust PSR algorithm for target reconstruction. We employed a scattering layer to replace conventional modulators and permanently integrated it with the image detector. Benefiting from the high-degree-of-freedom calibration, the scattering layer realized fine wavefront modulation with a small feature size. Besides, the integration engineering avoided repetitious calibration and reduced the complexity of data acquisition. The reported PSR algorithm combined both model-driven and data-driven strategies, with the advantages of high fidelity, strong generalization, and low computational complexity. The remarkable performance allows it to efficiently exploit the high-frequency information from the fine modulation. A series of experiments validate that the reported sensor and PSR algorithm provide a low-cost solution for large-scale microscopic imaging, realizing ∼1.1 μm imaging resolution and ∼7 dB enhancement on the PSNR index compared to existing methods

Complex-domain super-resolution imaging with distributed optimization

Complex-domain imaging has emerged as a valuable technique for investigating weak-scattered samples. However, due to the detector's pursuit of large pixel size for high throughput, the resolution limitation impedes its further development. In this work, we report a lensless on-chip complex-domain imaging system, together with a distributed-optimization-based pixel super-resolution technique (DO-PSR). The system employs a diffuser shifting to realize phase modulation and increases observation diversity. The corresponding DO-PSR technique derives an alternating projection operator and an enhancing neural network to tackle the measurement fidelity and statistical prior regularization subproblems. Extensive experiments show that the system outperforms the existing techniques with as much as 11dB on PSNR, and one-order-of-magnitude higher cell counting precision

Interfacial Gradient-Energy-Band-Alignment Modulation via a Vapor-Phase Anion-Exchange Reaction toward Lead-Free Perovskite Photodetectors with Excellent UV Imaging Capability

Bi-based inorganic perovskites have attracted great attention in optoelectronics, as they feature similar photoelectric properties but have high stability and lead-free merits. Unfortunately, due to the high exciton binding energy and small Bohr radius, their photodetection performance still largely lags behind that of Pb-based counterparts. Herein, using a vapor-phase chloride ion-substitution strategy, Cs3Bi2Br9 photodetectors (PDs) with gradient energy band alignment were delicately modulated, contributing to a high carrier separation/collection efficiency. The optimized Bi-based perovskite ACCT (Al2O3/Cs3Bi2Br9/Cs3Bi2ClxBr9–x/TiO2) PDs exhibit outstanding performance, the ON/OFF ratio and linear dynamic range (LDR) are significantly improved by 20 and 2.6 times, respectively. Significantly, we further demonstrate the high-SNR (signal-to-noise ratio) UV imaging based on the optimized device, which shows 21.887 dB higher than that of the pristine device. Finally, the vapor-phase anion-exchange modified perovskite PDs show long-term stability and high UV resistance. Vapor-phase ion-substitution is a promising approach for the synergistic effect of matched energy band alignment and interface passivation, which can be applied to other perovskite-based optoelectronic devices

Field-portable quantitative lensless microscopy based on translated speckle illumination and sub-sampled ptychographic phase retrieval

We report a compact, cost-effective and field-portable lensless imaging platform for quantitative microscopy. In this platform, the object is placed on top of an image sensor chip without using any lens. We use a low-cost galvo scanner to rapidly scan an unknown laser speckle pattern on the object. To address the positioning repeatability and accuracy issues, we directly recover the positional shifts of the speckle pattern based on the phase correlation of the captured images. To bypass the resolution limit set by the imager pixel size, we employ a sub-sampled ptychographic phase retrieval process to recover the complex object. We validate our approach using a resolution target, a phase target, and a biological sample. Our results show that accurate, high-quality complex images can be obtained from a lensless dataset with as few as ~10 images. We also demonstrate the reported approach to achieve a 6.4 mm by 4.6 mm field of view and a half pitch resolution of 1 miron. The reported approach may provide a quantitative lensless imaging strategy for addressing point-of-care, global-health, and telemedicine related challenges