As the basis of virtual content creation, cameras are integral to augmented reality (AR) applications. However, the opaque nature of the camera's appearance can prevent it from being integrated into a transparent AR display. Here we introduced an integrated, compact, and flexible see-through camera, which enables crucial functionalities like eye gaze tracking and eye-position perspective photography, enhancing the immersive experience and interaction possibilities.
Quantitative phase imaging (QPI) techniques are faced with an inherent trade-off between phase imaging fidelity and temporal resolution. Here, we propose a general algorithmic framework for QPI reconstruction that takes into account the spatiotemporal image priors. In particular, total variation with respect to the complex spatio-temporal datacube is introduced as a sparsity-promoting regularizer. The phase retrieval process is formulated as a standard optimization problem and is solved via an accelerated proximal gradient method. The algorithms are evaluated on a proof-of-concept QPI imaging system based on defocus diversity. Numerical and experimental results both indicate that the proposed spatio-temporal compressive phase retrieval framework could achieve high-fidelity quantitative phase imaging while improving the temporal resolution to that of a single-shot method. We experimentally demonstrate video-rate QPI of dynamic biological activities that is free of motion blur and twin-image artifacts. The proposed framework could potentially achieve a high space-bandwidth-time product and push the information throughput of QPI systems towards the theoretic limit.
Quantitative phase imaging (QPI) techniques can reveal the subtle interactions between light and the physical objects. However, the reconstruction problem is inherently ill-posed because only intensity can be directly recorded by the image sensor. Here, we propose a general computational framework for single-shot, high-quality QPI by exploring the sparsity features of the complex sample field. The resulting image reconstruction algorithm is highly scalable and features theoretically tractable convergence behaviors. We successfully demonstrate single-shot QPI in various metrological and biomedical applications. The proposed algorithmic framework can also be extended to exploit spatiotemporal priors and diversity measurement schemes, thereby pushing the imaging performance toward higher limits.
Lensless microscopy is an emerging imaging modality that overcomes the inherent limitation of conventional lens-based optics, especially in terms of imaging throughput, functionality, and cost-effectiveness. Pixel super-resolution phase retrieval serves as the key underlying technique for reconstructing high-resolution holographic images from the raw measurements. In this talk, we revisit lensless microscopy from a computational imaging perspective. A unified mathematical framework is established and the encoding and decoding mechanisms of the phase and subpixel information are analyzed. Regularization and Nesterov’s momentum techniques are introduced to speed up the data acquisition and reconstruction procedures, respectively. The proposed algorithms are verified through a proof-of-concept lensless on-chip microscope. We experimentally demonstrate the capability of pixel super-resolution phase retrieval techniques in revealing the subpixel and quantitative phase information of complex biological samples.
The imaging quality of in-line digital holography is challenged by the twin-image and aliasing effects because sensors only respond to intensity and pixels are of finite size. As a result, phase retrieval and pixel superresolution techniques serve as the two essential ingredients for high-fidelity holographic imaging. In this work, we combine the two within a unified algorithmic framework. Pixel super-resolution phase retrieval is recast as an optimization problem and is solved via gradient descent-based algorithms. Regularization techniques and Nesterov’s momentum are introduced to further speed up data acquisition and iterative reconstruction. The proposed algorithms are verified through a proof-of-concept lensless on-chip microscope. We demonstrate experimentally the capability of pixel super-resolution phase retrieval techniques in revealing the subpixel and quantitative phase information of complex biological samples.
Existing quantitative phase imaging (QPI) techniques are faced with an inherent trade-off between phase imaging fidelity and temporal resolution. Here, we propose a general algorithmic framework for QPI reconstruction that enables frame-rate-limited holographic imaging. It takes an inverse problem approach by formulating phase retrieval as a nonsmooth nonconvex optimization problem. Efficient solvers for the problem are derived whose algorithmic behaviors have been studied from both theoretical and experimental perspectives. The proposed framework is applicable to various existing holographic imaging configurations, and makes it possible to incorporate advanced image priors for quality enhancement.
Objective measurements of the morphology and dynamics of label-free cells and tissues can be achieved by quantitative phase. Modern quantitative optical imaging possesses a huge information capacity. But the bandwidth of quantitative phase imaging is technically limited in an interferometric setup, thereby constraining the throughput in label-free phase imaging. Firstly, we demonstrate a high-bandwidth holographic microscopy which exploits high-throughput label-free quantitative phase image. We introduce Kramers–Kronig relations to the off-axis multiplexing technology. Based the analyticity of band-limited signal under diffraction-limited system, the maximum space bandwidth utilization in single multiplexing hologram is increased to 78.5%. Secondly, by assisting with off-axis optimized initial phase in the phase retrieval, high-resolution and full-field reconstruction by exploiting the full bandwidth are demonstrated for complex-amplitude reconstruction. Off-axis optimization phase provides an effective initial guess to avoid stagnation and minimize the required measurements of multi-plane phase retrieval. Different tumor types and a variety of precursor lesions and pathologies can be visualized with label-free specimens.
Quantitative phase imaging with high resolution remains a long-term pursuit of many biomedical applications. However, the performance of coherent imaging systems is challenged by the intensity-only measurement mechanism and the sampling limit of the pixels. In this work, we introduce an imaging system that achieves pixel super-resolution quantitative phase imaging based on modulation diversity. A programmable phase-only spatial light modulator is used to generate various phase modulation patterns to the wavefront, providing data diversity for phase recovery at subpixel resolution. The system requires no mechanical displacements, enabling high-speed image acquisition, providing a competitive approach to high-throughput quantitative phase imaging applications.
The imaging quality of in-line digital holography is challenged by the twin-image and aliasing effects because sensors only respond to intensity and pixels are of finite size. As a result, phase retrieval and pixel super-resolution techniques serve as the two essential ingredients for high-fidelity holographic imaging. In this work, we combine the two as a unified optimization problem, and propose a generalized algorithmic framework for pixel-super-resolved phase retrieval. In particular, we introduce the iterative projection algorithms and gradient descent algorithms for solving this problem. The basic building blocks, namely the projection operator and the Wirtinger gradient, are derived and analyzed. The algorithms are verified with both simulated and experimental data. The proposed framework generalizes well to various physical settings, and is compatible with many state-of-the-art optimization algorithms.
The imaging quality of inline digital holography is challenged by the twin-image artefact because the phase retrieval problem is severely ill-conditioned. Sparsity-promoting regularizers such as the total variation (TV) seminorms have been explored to tackle the ill-posedness and proved effective in modeling real-world objects. However, previous works are mainly based on the TV seminorms for real-valued images, which limit their application in digital holography where we are often dealing with complex-valued signals. In this work, we introduce the complex constrained TV regularizers and propose an efficient proximal gradient algorithm for solving the phase retrieval problem. The proposed complex TV model and the corresponding algorithm are verified by numerical and experimental results. We believe that the proposed algorithmic framework can cast new light on solving a large class of optimization problems based on complex constrained TV regularization.
Lensless holographic imaging is challenged by the twin-image artifact due to the missing phase and the aliasing effect due to the undersampled measurement. Therefore, phase retrieval and pixel super-resolution (PSR) techniques serve as the essential ingredients for high-fidelity holographic imaging. In this work, we combine the two in a unified framework by formulating the PSR phase retrieval as a non-convex feasibility problem. An adaptive smoothing strategy for escaping local minima is introduced. Numerical and experimental results are presented and discussed. The proposed framework can be generalized to various physical settings, and is compatible with the state-of-the-art iterative projection algorithms.
Reliable phase-only spatial light modulators (SLMs) are in demand for accurate phase modulation. However, the nonlinear optical response of liquid crystals and the limited manufacturing process can lead to the spatial nonuniformity of the phase modulation of the SLM. The transfer from the grayscale to the modulated phase can be different from the lookup table (LUT) shown in the SLM manual. The SLM should be measured for calibration. We propose a calibration method based on digital holography to calibrate the spatial nonuniformity of phase modulation of the SLM. Using a self-generated grating, the SLM involved system is converted to the calibration system based on the principle of digital holography. The in-situ strategy for low cost and efficient calibration was demonstrated with optical experiments using a 4K (3840 × 2160 pixels) phase-only SLM. The spatial nonuniformity was calibrated to decrease by more than 75% using only a beam splitter and an imaging sensor.
Reliable phase-only spatial light modulators (SLMs) are in demand for accurate phase modulation in a wide range of fields. Due to the nonlinear optical response of liquid crystals and the limited manufacturing process available, the spatial nonuniformity of the phase modulation by the pixels should be measured and/or calibrated. We propose an in situ calibration method based on digital holography to calibrate the spatial nonuniformity of phase modulation of the SLM. The SLM panel is divided into blocks composed of pixels. The differential phase on hundreds of blocks can be reconstructed through the holograms. The distribution of modulated phase can then be derived after eliminating statics phase anomalies. The spatial nonuniformity of the panel can be measured for calibration with high efficiency. A modulated phase step on the SLM was calibrated to increase linearly. The spatial nonuniformity was calibrated to decrease by more than 75% using only a beam splitter and an imaging sensor. The in situ strategy for low cost and efficient calibration was demonstrated with optical experiments using a 4K (3840 × 2160 pixels) phase-only SLM.
We propose a self-reference interferometric method for phase calibration of spatial light modulator (SLM) based on two blazed gratings. Compared with traditional methods, the proposed method yields more stable results by generating fringes with lower spatial frequency, thus making it possible for accurate, low cost measurement of the phase modulation of an SLM.
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