Single-shot inline holography using a physics-aware diffusion model.

Among holographic imaging configurations, inline holography excels in its compact design and portability, making it the preferred choice for on-site or field applications with unique imaging requirements. However, effectively holographic reconstruction from a single-shot measurement remains a challenge. While several approaches have been proposed, our novel unsupervised algorithm, the physics-aware diffusion model for digital holographic reconstruction (PadDH), offers distinct advantages. By seamlessly integrating physical information with a pre-trained diffusion model, PadDH overcomes the need for a holographic training dataset and significantly reduces the number of parameters involved. Through comprehensive experiments using both synthetic and experimental data, we validate the capabilities of PadDH in reducing twin-image contamination and generating high-quality reconstructions. Our work represents significant advancements in unsupervised holographic imaging by harnessing the full potential of the pre-trained diffusion prior.

Single-shot inline holography using a physics-aware diffusion model: errata.

We present an erratum to our paper [Opt. Express32, 10444 (2024)10.1364/OE.517233]. This erratum aims to address an unintentional error in the methodology presented in Part 3.1. The error may lead to confusion among readers, and we provide additional clarification to ensure a comprehensive understanding of the technique. It is important to note that these corrections do not affect the results or conclusions of the original work.

Efficient non-line-of-sight tracking with computational neuromorphic imaging.

Non-line-of-sight (NLOS) sensing is an emerging technique that is capable of detecting objects hidden behind a wall, around corners, or behind other obstacles. However, NLOS tracking of moving objects is challenging due to signal redundancy and background interference. Here, we demonstrate computational neuromorphic imaging with an event camera for NLOS tracking, unaffected by the relay surface, which can efficiently obtain non-redundant information. We show how this sensor, which responds to changes in luminance within dynamic speckle fields, allows us to capture the most relevant events for direct motion estimation. The experimental results confirm that our method has superior performance in terms of efficiency, and accuracy, which greatly benefits from focusing on well-defined NLOS object tracking.

Typical Tire Additives in River Water: Leaching, Transformation, and Environmental Risk Assessment.

Tire wear particles (TWPs) released during vehicle driving can enter water bodies, leading to leaching of tire additives (TAs) in aquatic environments. However, the transformation behavior and related ecological impacts of TAs and their transformation products (TPs) remain unclear. In this study, laboratory-based simulation experiments and field investigations were conducted to explore the transformation mechanisms and ecological risks of TAs. After being placed in river water for 24 h, about 7-95% of 12 investigated TAs in TWPs were leached. Forty-eight TPs from eight TAs were tentatively identified along with different transformation pathways via suspect screening by high-resolution mass spectrometry. Semiquantitative results indicated that TPs derived from N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylene-diamine (6PPD) were predominant in leachates, while aryl hydrolysis and quinone pathways were the main transformation pathways. Field investigations on urban surface water samples from 16 sites in Hong Kong revealed the occurrence of 17 TAs and 1 TP, with concentrations ranging from 13.9 to 2230 ng/L (median ± standard deviation: 226 ± 534 ng/L). Sixteen TPs from six TAs were additionally identified via suspect screening. It is estimated that 6PPD-quinone and seven TAs could pose medium to high ecological risk, while N-(1,3-dimethylbutyl)-N'-phenyl-p-quinonediimine, a frequently detected TP, was identified as a persistent-bioaccumulative-toxic substance.

Phase retrieval with a dual recursive scheme.

Since optical sensors cannot detect the phase information of the light wave, recovering the missing phase from the intensity measurements, called phase retrieval (PR), is a natural and important problem in many imaging applications. In this paper, we propose a learning-based recursive dual alternating direction method of multipliers, called RD-ADMM, for phase retrieval with a dual and recursive scheme. This method tackles the PR problem by solving the primal and dual problems separately. We design a dual structure to take advantage of the information embedded in the dual problem that can help with solving the PR problem, and we show that it is feasible to use the same operator for both the primal and dual problems for regularization. To demonstrate the efficiency of this scheme, we propose a learning-based coded holographic coherent diffractive imaging system to generate the reference pattern automatically according to the intensity information of the latent complex-valued wavefront. Experiments on different kinds of images with a high noise level indicate that our method is effective and robust, and can provide higher-quality results than other commonly-used PR methods for this setup.

Unifying temporal phase unwrapping framework using deep learning.

Temporal phase unwrapping (TPU) is significant for recovering an unambiguous phase of discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Generally, temporal phase unwrapping algorithms can be classified into three groups: the multi-frequency (hierarchical) approach, the multi-wavelength (heterodyne) approach, and the number-theoretic approach. For all of them, extra fringe patterns of different spatial frequencies are required for retrieving the absolute phase. Due to the influence of image noise, people have to use many auxiliary patterns for high-accuracy phase unwrapping. Consequently, image noise limits the efficiency and the measurement speed greatly. Further, these three groups of TPU algorithms have their own theories and are usually applied in different ways. In this work, for the first time to our knowledge, we show that a generalized framework using deep learning can be developed to perform the TPU task for different groups of TPU algorithms. Experimental results show that benefiting from the assistance of deep learning the proposed framework can mitigate the impact of noise effectively and enhance the phase unwrapping reliability significantly without increasing the number of auxiliary patterns for different TPU approaches. We believe that the proposed method demonstrates great potential for developing powerful and reliable phase retrieval techniques.

Iterative phase retrieval with a sensor mask.

As an important inverse imaging problem in diffraction optics, Fourier phase retrieval aims at estimating the latent image of the target object only from the magnitude of its Fourier measurement. Although in real applications alternating methods are widely-used for Fourier phase retrieval considering the constraints in the object and Fourier domains, they need a lot of initial guesses and iterations to achieve reasonable results. In this paper, we show that a proper sensor mask directly attached to the Fourier magnitude can improve the efficiency of the iterative phase retrieval algorithms, such as alternating direction method of multipliers (ADMM). Furthermore, we refer to the learning-based method to determine the sensor mask according to the Fourier measurement, and unrolled ADMM is used for phase retrieval. Numerical results show that our method outperforms other existing methods for the Fourier phase retrieval problem.

Temporal compressive imaging reconstruction based on a 3D-CNN network.

In temporal compressive imaging (TCI), high-speed object frames are reconstructed from measurements collected by a low-speed detector array to improve the system imaging speed. Compared with iterative algorithms, deep learning approaches utilize a trained network to reconstruct high-quality images in a short time. In this work, we study a 3D convolutional neural network for TCI reconstruction to make full use of the temporal and spatial correlation among consecutive object frames. Both simulated and experimental results demonstrate that our network can achieve better reconstruction quality with fewer number of layers.

Lens-free motion analysis via neuromorphic laser speckle imaging.

Laser speckle imaging (LSI) is a powerful tool for motion analysis owing to the high sensitivity of laser speckles. Traditional LSI techniques rely on identifying changes from the sequential intensity speckle patterns, where each pixel performs synchronous measurements. However, a lot of redundant data of the static speckles without motion information in the scene will also be recorded, resulting in considerable resources consumption for data processing and storage. Moreover, the motion cues are inevitably lost during the "blind" time interval between successive frames. To tackle such challenges, we propose neuromorphic laser speckle imaging (NLSI) as an efficient alternative approach for motion analysis. Our method preserves the motion information while excluding the redundant data by exploring the use of the neuromorphic event sensor, which acquires only the relevant information of the moving parts and responds asynchronously with a much higher sampling rate. This neuromorphic data acquisition mechanism captures fast-moving objects on the order of microseconds. In the proposed NLSI method, the moving object is illuminated using a coherent light source, and the reflected high frequency laser speckle patterns are captured with a bare neuromorphic event sensor. We present the data processing strategy to analyze motion from event-based laser speckles, and the experimental results demonstrate the feasibility of our method at different motion speeds.

Holographic 3D particle reconstruction using a one-stage network.

Volumetric reconstruction of a three-dimensional (3D) particle field with high resolution and low latency is an ambitious and valuable task. As a compact and high-throughput imaging system, digital holography (DH) encodes the 3D information of a particle volume into a two-dimensional (2D) interference pattern. In this work, we propose a one-stage network (OSNet) for 3D particle volumetric reconstruction. Specifically, by a single feed-forward process, OSNet can retrieve the 3D coordinates of the particles directly from the holograms without high-fidelity image reconstruction at each depth slice. Evaluation results from both synthetic and experimental data confirm the feasibility and robustness of our method under different particle concentrations and noise levels in terms of detection rate and position accuracy, with improved processing speed. The additional applications of 3D particle tracking are also investigated, facilitating the analysis of the dynamic displacements and motions for micro-objects or cells. It can be further extended to various types of computational imaging problems sharing similar traits.

Deep learning for digital holography: a review.

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities for DH. Here, we survey recent developments in various DH applications powered by deep learning algorithms. This article starts with a brief introduction to digital holographic imaging, then summarizes the most relevant deep learning techniques for DH, with discussions on their benefits and challenges. We then present case studies covering a wide range of problems and applications in order to highlight research achievements to date. We provide an outlook of several promising directions to widen the use of deep learning in various DH applications.

Broad dual-band temporal compressive imaging with optical calibration.

For applications such as remote sensing and bio-imaging, images from multiple bands can provide much richer information compared to a single band. However, most multispectral imaging systems have difficulty in acquiring images for high-speed moving objects. In this paper, we use a DMD-based temporal compressive imaging (TCI) system to obtain high-speed images of moving objects over a broad dual-band spectral range, in the visible and the near-infrared (NIR) bands simultaneously. To deal with the degraded reconstruction caused by the optics, four nonuniform calibration strategies are studied, which can also be implemented into other compressive imaging systems. Moving objects covered by paint or through a diffuser are reconstructed to demonstrate the superior performance of the calibrated broad dual-band TCI system.

Event-based laser speckle correlation for micro motion estimation: erratum.

We present an erratum to our Letter [Opt. Lett.46, 3885 (2021)OPLEDP0146-959210.1364/OL.430419]. This erratum corrects an inadvertent error in Eq. (4). The corrections have no influence on the results and conclusions of the original Letter.

Event-based laser speckle correlation for micro motion estimation.

Micro motion estimation has important applications in various fields such as microfluidic particle detection and biomedical cell imaging. Conventional methods analyze the motion from intensity images captured using frame-based imaging sensors such as the complementary metal-oxide semiconductor (CMOS) and the charge-coupled device (CCD). Recently, event-based sensors have evolved with the special capability to record asynchronous light changes with high dynamic range, high temporal resolution, low latency, and no motion blur. In this Letter, we explore the potential of using the event sensor to estimate the micro motion based on the laser speckle correlation technique.

Digital holographic imaging and classification of microplastics using deep transfer learning.

We devise an inline digital holographic imaging system equipped with a lightweight deep learning network, termed CompNet, and develop the transfer learning for classification and analysis. It has a compression block consisting of a concatenated rectified linear unit (CReLU) activation to reduce the channels, and a class-balanced cross-entropy loss for training. The method is particularly suitable for small and imbalanced datasets, and we apply it to the detection and classification of microplastics. Our results show good improvements both in feature extraction, and generalization and classification accuracy, effectively overcoming the problem of overfitting. This method could be attractive for future in situ microplastic particle detection and classification applications.

Dynamic laser speckle analysis using the event sensor.

Dynamic laser speckle analysis (DLSA) can obtain useful information about the scene dynamics. Traditional implementations use intensity-based imaging sensors such as a complementary metal oxide semiconductor and charge-coupled device to capture time-varying intensity frames. We use an event sensor that measures pixel-wise asynchronous brightness changes to record speckle pattern sequences. Our approach takes advantage of the low latency and high contrast sensitivity of the event sensor to implement DLSA with high temporal resolution. We also propose two evaluation metrics designed especially for event data. Comparison experiments are conducted in identical conditions to demonstrate the feasibility of our proposed approach.

Acceleration of autofocusing with improved edge extraction using structure tensor and Schatten norm.

Determining the optimal focal plane amongst a stack of blurred images in a short response time is a non-trivial task in optical imaging like microscopy and photography. An autofocusing algorithm, or in other words, a focus metric, is key to effectively dealing with such problem. In previous work, we proposed a structure tensor-based autofocusing algorithm for coherent imaging, i.e., digital holography. In this paper, we further extend the realm of this method in more imaging modalities. With an optimized computation scheme of structure tensor, a significant acceleration of about fivefold in computation speed without sacrificing the autofocusing accuracy is achieved by using the Schatten matrix norm instead of the vector norm. Besides, we also demonstrate its edge extraction capability by retrieving the intermediate tensor image. Synthesized and experimental data acquired in various imaging scenarios such as incoherent microscopy and photography are demonstrated to verify the efficacy of this method.

Computational Optical Sensing and Imaging: feature issue introduction.

This Feature Issue includes 19 articles that highlight advances in the field of Computational Optical Sensing and Imaging. Many of the articles were presented at the 2019 OSA Topical Meeting on Computational Optical Sensing and Imaging held in Munich, Germany, on June 24-27. Articles featured in the issue cover a broad array of topics ranging from imaging through scattering media, imaging round corners and compressive imaging to machine learning for recovery of images.

RedCap: residual encoder-decoder capsule network for holographic image reconstruction.

A capsule network, as an advanced technique in deep learning, is designed to overcome information loss in the pooling operation and internal data representation of a convolutional neural network (CNN). It has shown promising results in several applications, such as digit recognition and image segmentation. In this work, we investigate for the first time the use of capsule network in digital holographic reconstruction. The proposed residual encoder-decoder capsule network, which we call RedCap, uses a novel windowed spatial dynamic routing algorithm and residual capsule block, which extends the idea of a residual block. Compared with the CNN-based neural network, RedCap exhibits much better experimental results in digital holographic reconstruction, while having a dramatic 75% reduction in the number of parameters. It indicates that RedCap is more efficient in the way it processes data and requires a much less memory storage for the learned model, which therefore makes it possible to be applied to some challenging situations with limited computational resources, such as portable devices.

Axially resolved volumetric two-photon microscopy with an extended field of view using depth localization under mirrored Airy beams.

It is a great challenge in two-photon microscopy (2PM) to have a high volumetric imaging speed without sacrificing the spatial and temporal resolution in three dimensions (3D). The structure in 2PM images could be reconstructed with better spatial and temporal resolution by the proper choice of the data processing algorithm. Here, we propose a method to reconstruct 3D volume from 2D projections imaged by mirrored Airy beams. We verified that our approach can achieve high accuracy in 3D localization over a large axial range and is applicable to continuous and dense sample. The effective field of view after reconstruction is expanded. It is a promising technique for rapid volumetric 2PM with axial localization at high resolution.