Single-shot lensless imaging via simultaneous multi-angle LED illumination

Differentiable pixel-super-resolution lensless imaging

Conventional lensless imaging systems require complex phase diversity measurements and sequential processing steps, limiting their practical application despite their compact design. We present a differentiable end-to-end pixel-super-resolution (dPSR) technique that unifies PSR hologram synthesis, autofocusing, and complex-field reconstruction within a single optimization framework. By jointly optimizing these traditionally separate processes, our method eliminates both phase diversity requirements and error accumulation from sequential processing. Our method achieves superior position estimation accuracy (mean error 0.0282 pixels versus 0.1172 pixels with conventional methods), delivering precise autofocusing with accuracy better than 0.3 µm, and enabling a twofold resolution enhancement beyond the sensor's native pixel size. This robust performance is validated through both simulated and experimental results, including challenging phase objects and label-free cell imaging, establishing dPSR as a practical solution for high-resolution microscopy applications.

Deep nonlocal low-rank regularization for complex-domain pixel super-resolution

Pixel super-resolution (PSR) has emerged as a promising technique to break the sampling limit for phase imaging systems. However, due to the inherent nonconvexity of phase retrieval problem and super-resolution process, PSR algorithms are sensitive to noise, leading to reconstruction quality inevitably deteriorating. Following the plug-and-play framework, we introduce the nonlocal low-rank (NLR) regularization for accurate and robust PSR, achieving a state-of-the-art performance. Inspired by the NLR prior, we further develop the complex-domain nonlocal low-rank network (CNLNet) regularization to perform nonlocal similarity matching and low-rank approximation in the deep feature domain rather than the spatial domain of conventional NLR. Through visual and quantitative comparisons, CNLNet-based reconstruction shows an average 1.4 dB PSNR improvement over conventional NLR, outperforming existing algorithms under various scenarios.

Depth-multiplexed ptychographic microscopy for high-throughput imaging of stacked bio-specimens on a chip

Imaging a large number of bio-specimens at high speed is essential for many biomedical applications. The common strategy is to place specimens at different lateral positions and image them sequentially. Here we report a new on-chip imaging strategy, termed depth-multiplexed ptychographic microscopy (DPM), for parallel imaging and sensing at high speed. Different from the common strategy, DPM stacks multiple specimens in the axial direction and images the entire z-stack all at once. In our prototype platform, we modify a low-cost car mirror for programmable steering of the incident laser beam. A blood-coated image sensor is then placed underneath the stacked sample for acquiring the resulting diffraction patterns. With the captured images, we perform blind recovery of the incident beam angle and model different layers of the stacked sample as different coded surfaces for object reconstruction. For in vitro experiment, we demonstrate time-lapse cell culture monitoring by imaging 3 stacked microfluidic channels on the coded sensor. For high-throughput cytometric analysis, we image 5 stacked brain sections with a 205-mm² field of view in ∼50 s. Cytometric analysis is also performed to quantify the cellular proliferation biomarkers on the slides. The DPM approach adds a new degree of freedom for data multiplexing in microscopy, enabling parallel imaging of multiple specimens using a single detector. The demonstrated 6-mm depth of field is among the longest ones in microscopy imaging. The novel depth-multiplexed configuration also complements the miniaturization provided by microfluidics devices, offering a solution for on-chip sensing and imaging with efficient sample handling.

Optical ptychography for biomedical imaging: recent progress and future directions [Invited]

Ptychography is an enabling microscopy technique for both fundamental and applied sciences. In the past decade, it has become an indispensable imaging tool in most X-ray synchrotrons and national laboratories worldwide. However, ptychography's limited resolution and throughput in the visible light regime have prevented its wide adoption in biomedical research. Recent developments in this technique have resolved these issues and offer turnkey solutions for high-throughput optical imaging with minimum hardware modifications. The demonstrated imaging throughput is now greater than that of a high-end whole slide scanner. In this review, we discuss the basic principle of ptychography and summarize the main milestones of its development. Different ptychographic implementations are categorized into four groups based on their lensless/lens-based configurations and coded-illumination/coded-detection operations. We also highlight the related biomedical applications, including digital pathology, drug screening, urinalysis, blood analysis, cytometric analysis, rare cell screening, cell culture monitoring, cell and tissue imaging in 2D and 3D, polarimetric analysis, among others. Ptychography for high-throughput optical imaging, currently in its early stages, will continue to improve in performance and expand in its applications. We conclude this review article by pointing out several directions for its future development.

Integration of Fourier ptychography with machine learning: an alternative scheme

As the core task of the reconstruction in conventional ptychography (CP) and Fourier ptychographic microscopy (FPM), the meticulous design of ptychographical iterative engine (PIE) largely affects the performance of reconstruction algorithms. Compared to traditional PIE algorithms, the paradigm of combining with machine learning to cross a local optimum has recently achieved significant progress. Nevertheless, existing designed engines still suffer drawbacks such as excessive hyper-parameters, heavy tuning work and lack of compatibility, which greatly limit their practical applications. In this work, we present a complete set of alternative schemes comprised of a kind of new perspective, a uniform design template, and a fusion framework, to naturally integrate Fourier ptychography (FP) with machine learning concepts. The new perspective, Dynamic Physics, is taken as the preferred tool to analyze a path (algorithm) at the physical level; the uniform design template, T-FP, clarifies the physical significance and optimization part in a path; the fusion framework follows two workable guidelines that are specially designed to keep convergence and make later localized modification for a new path, and further establishes a link between FP iterations and the gradient update in machine learning. Our scheme is compatible with both traditional FP paths and machine learning concepts. By combining ideas in both fields, we offer two design examples, MaFP and AdamFP. Results for both simulations and experiments show that designed algorithms following our scheme obtain better, faster (converge at the early stage after a few iterations) and more stable recovery with only minimal tuning hyper-parameters, demonstrating the effectiveness and superiority of our scheme.

Lensfree on-chip microscopy based on single-plane phase retrieval

We propose a novel single-plane phase retrieval method to realize high-quality sample reconstruction for lensfree on-chip microscopy. In our method, complex wavefield reconstruction is modeled as a quadratic minimization problem, where total variation and joint denoising regularization are designed to keep a balance of artifact removal and resolution enhancement. In experiment, we built a 3D-printed field-portable platform to validate the imaging performance of our method, where resolution chart, dynamic target, transparent cell, polystyrene beads, and stained tissue sections are employed for the imaging test. Compared to state-of-the-art methods, our method eliminates image degradation and obtains a higher imaging resolution. Different from multi-wavelength or multi-height phase retrieval methods, our method only utilizes a single-frame intensity data record to accomplish high-fidelity reconstruction of different samples, which contributes a simple, robust, and data-efficient solution to design a resource-limited lensfree on-chip microscope. We believe that it will become a useful tool for telemedicine and point-of-care application.

Blood-Coated Sensor for High-Throughput Ptychographic Cytometry on a Blu-ray Disc

The Blu-ray drive is an engineering masterpiece that integrates disc rotation, pickup head translation, and three lasers in a compact and portable format. Here, we integrate a blood-coated image sensor with a modified Blu-ray drive for high-throughput cytometric analysis of various biospecimens. In this device, samples are mounted on the rotating Blu-ray disc and illuminated by the built-in lasers from the pickup head. The resulting coherent diffraction patterns are then recorded by the blood-coated image sensor. The rich spatial features of the blood-cell monolayer help down-modulate the object information for sensor detection, thus forming a high-resolution computational biolens with a theoretically unlimited field of view. With the acquired data, we develop a lensless coherent diffraction imaging modality termed rotational ptychography for image reconstruction. We show that our device can resolve the 435 nm line width on the resolution target and has a field of view only limited by the size of the Blu-ray disc. To demonstrate its applications, we perform high-throughput urinalysis by locating disease-related calcium oxalate crystals over the entire microscope slide. We also quantify different types of cells on a blood smear with an acquisition speed of ∼10,000 cells per second. For in vitro experiments, we monitor live bacterial cultures over the entire Petri dish with single-cell resolution. Using biological cells as a computational lens could enable new intriguing imaging devices for point-of-care diagnostics. Modifying a Blu-ray drive with the blood-coated sensor further allows the spread of high-throughput optical microscopy from well-equipped laboratories to citizen scientists worldwide.

Robust multi-angle structured illumination lensless microscopy via illumination angle calibration

Multi-angle structured illumination lensless (MASIL) microscopy enables high-resolution image recovery over a large field of view. Successful image recovery of MASIL microscopy, however, relies on an accurate knowledge of the multi-angle illumination. System misalignments and slight deviations from the true illumination angle may result in image artifacts in reconstruction. Here we report a MASIL microscopy system that is robust against illumination misalignment. To calibrate the illumination angles, we design and use a double-sided mask, which is a glass wafer fabricated with a ring-array pattern on the upper surface and a disk-array pattern on the lower surface. As such, the illumination angles can be decoded from the captured images by estimating the relative displacement of the two patterns. We experimentally demonstrate that this system can achieve successful image recovery without any prior knowledge of the illumination angles. The reported approach provides a simple yet robust resolution for wide-field lensless microscopy. It can solve the LED array misalignment problem and calibrate angle-varied illumination for a variety of applications.

Ptychography-based high-throughput lensless on-chip microscopy via incremental proximal algorithms

Ptychography-based lensless on-chip microscopy enables high-throughput imaging by retrieving the missing phase information from intensity measurements. Numerous reconstruction algorithms for ptychography have been proposed, yet only a few incremental algorithms can be extended to lensless on-chip microscopy because of large-scale datasets but limited computational efficiency. In this paper, we propose the use of accelerated proximal gradient methods for blind ptychographic phase retrieval in lensless on-chip microscopy. Incremental gradient approaches are adopted in the reconstruction routine. Our algorithms divide the phase retrieval problem into sub-problems involving the evaluation of proximal operator, stochastic gradient descent, and Wirtinger derivatives. We benchmark the performances of accelerated proximal gradient, extended ptychographic iterative engine, and alternating direction method of multipliers, and discuss their convergence and accuracy in both noisy and noiseless cases. We also validate our algorithms using experimental datasets, where full field of view measurements are captured to recover the high-resolution complex samples. Among these algorithms, accelerated proximal gradient presents the overall best performance regarding accuracy and convergence rate. The proposed methods may find applications in ptychographic reconstruction, especially for cases where a wide field of view and high resolution are desired at the same time.

Generalized optimization framework for pixel super-resolution imaging in digital holography

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 two essential ingredients for high-fidelity and high-resolution 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. As an example, the Wirtinger gradient descent algorithm for pixel-super-resolved phase retrieval, termed as Wirtinger-PSR, is proposed and compared with the classical error-reduction algorithm. The Wirtinger-PSR algorithm is verified with both simulated and experimental data. The proposed framework generalizes well to various physical settings and helps bridging the gap between empirical studies and theoretical analyses.

Mask-modulated lensless imaging via translated structured illumination

Lensless microscopy technique enables high-resolution image recovery over a large field of view. By integrating the concept of phase retrieval, it can also retrieve the lost phase information from intensity-only measurements. Here we report a mask-modulated lensless imaging platform based on translated structured illumination. In the reported platform, we sandwich the object in-between a coded mask and a naked image sensor for lensless data acquisition. An LED array is used to provide angle-varied illumination for projecting a translated structured pattern without involving mechanical scanning. For different LED elements, we acquire the lensless intensity data for recovering the complex-valued object. In the reconstruction process, we employ the regularized ptychographic iterative engine and implement an up-sampling process in the reciprocal space. As demonstrated by experimental results, the reported platform is able to recover complex-valued object images with higher resolution and better quality than previous implementations. Our approach may provide a cost-effective solution for high-resolution and wide field-of-view ptychographic imaging without involving mechanical scanning.

Single-frame far-field diffractive imaging with randomized illumination

We introduce a single-frame diffractive imaging method called randomized probe imaging (RPI). In RPI, a sample is illuminated by a structured probe field containing speckles smaller than the sample's typical feature size. Quantitative amplitude and phase images are then reconstructed from the resulting far-field diffraction pattern. The experimental geometry of RPI is straightforward to implement, requires no near-field optics, and is applicable to extended samples. When the resulting data are analyzed with a complimentary algorithm, reliable reconstructions which are robust to missing data are achieved. To realize these benefits, a resolution limit associated with the numerical aperture of the probe-forming optics is imposed. RPI therefore offers an attractive modality for quantitative X-ray phase imaging when temporal resolution and reliability are critical but spatial resolution in the tens of nanometers is sufficient. We discuss the method, introduce a reconstruction algorithm, and present two proof-of-concept experiments: one using visible light, and one using soft X-rays.

AI on a chip

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.

Pixel super-resolved lens-free on-chip microscopy based on dual laterally shifting modulation

Achieving high spatial resolution over a wide field of view (FOV) is the goal of many imaging systems. In a traditional lens-based microscope, designing a complex objective with high numerical aperture (NA) to achieve this goal is a tough and challenging task. The lens-free wide-field imaging method based on phase retrieval provides a new way to bypass the trade-off between the spatial resolution and FOV of conventional microscopy. However, the typical lens-free microscopy usually requires mechanical devices with high precision and repeatability. In this paper, we report a robust and cost-effective pixel super-resolved lens-free imaging method based on dual laterally shifting modulation. A thin diffuser is inserted between the object and the image sensor to be used as the modulator. The diffuser and the object are transversely scanned at the same time to add diversities for phase retrieval and pixel super-resolution, respectively. In this way, the positional shifts of the diffuser and the object can be directly recovered with the registration algorithm, thus addressing the low stability and inaccuracy issues of translation stages. We also propose a pixel super-resolution phase-retrieval algorithm to recover the object and the unknown diffuser. We first use numerical simulations to evaluate the proposed scheme. Then, we validate this approach by imaging a resolution target and a pollen sample, thus achieving an FOV of ${\sim}{30}\;{\text{mm}^2}$∼30mm² and a half-pitch resolution of 0.78 µm, which surpasses 2.14 times the theoretical Nyquist-Shannon sampling resolution limit. Finally, the 3D refocusing ability is also verified by imaging a thick mosquito sample.