Virtual staining techniques enable the digital transformation of label-free images into clinically standardized stained images. However, the high costs and time involved in generating labeled datasets for training, combined with the absence of accelerated inference pipelines for high-throughput histopathology workflows remain major challenges to their widespread adoption in clinical practice. To overcome these limitations, we present a hardware-software co-designed system that integrates high-speed Fourier ptychographic microscopy with learned illumination, supported by a semi-supervised learning framework. Our end-to-end approach employs a learned multiplexed illumination strategy that significantly reduces acquisition time while maintaining high spatial resolution across a wide field of view. On the algorithmic side, a multi-stage neural network decouples phase reconstruction from colorization, and a contrastive learning framework further generalize the virtual staining by encouraging the network to focus on intrinsic tissue features rather than absorption-induced variations. Extensive experimental results confirm the effectiveness of our method, demonstrating accurate virtual staining of label-free images while providing a scalable and cost-effective alternative to traditional histochemical staining.

Photodetectors sensitive to the near-infrared (NIR) range are of great importance for applications in machine vision, autonomous driving, and three-dimensional (3D) imaging under low-light or adverse weather conditions. Recently, two-dimensional (2D) semiconductors have emerged as promising light-absorbing materials for NIR detection. However, the inevitable majority-carrier injection from the electrode fundamentally leads to high dark current and poor light-detection capability, which has constrained the development of 2D semiconductor photodetectors so far. Here, we demonstrate a high-detectivity NIR photodetector based on a MoTe2/WSe2/Cl-SnSe2 unipolar barrier heterostructure. The WSe2 layer acts as an efficient majority-carrier-blocking barrier, effectively suppressing dark current and achieving a room-temperature specific detectivity of 6.75 × 108 Jones, a linear dynamic range of 62 dB, and an effective -3 dB bandwidth approaching 0.1 MHz under 1310 nm illumination. Using this device as an imager, we show 3D NIR imaging of real objects under low-illumination conditions, highlighting its potential for applications such as vein visualization and material identification.

Lensless imaging enables compact and versatile computational cameras by replacing bulky optics with thin coded elements. However, reconstruction from the resulting measurements is challenging: large-footprint point-spread functions (PSFs) produce highly multiplexed observations, making inversion severely ill-conditioned and sensitive to calibration errors and model mismatch. While deep learning approaches, including hybrid models that incorporate physics priors, have shown promise, explicitly maintaining data fidelity throughout the network hierarchy remains difficult. Here, we propose the Integrated Forward-Inverse Network (IFIN), a physics-guided architecture that interleaves differentiable forward projections with learnable inverse updates at every scale, enabling complementary cues to be exploited jointly in the measurement and image domains. This bidirectional coupling supports progressive, physics-consistent refinement and permits system-constrained PSF kernel adaptation under model uncertainty. On challenging lensless benchmarks, including a newly introduced dataset, IFIN achieves state-of-the-art reconstruction quality. We further observe competitive performance on Gaussian deblurring and simulated inline holography reconstruction, suggesting that the same interleaving principle can extend beyond lensless cameras.

To rapidly and cost-effectively re-stain faded histological specimens, we present a high-speed virtual re-staining pipeline that combines near-infrared quantitative phase imaging utilizing Fourier ptychography and a trained deep neural network. Our supervised virtual re-staining model comprises two stages: a raw-to-phase module that replaces iterative FP reconstruction, and a phase-to-color module that synthesizes histologic RGB images for virtual staining. Because hematoxylin and eosin absorption is weak in the near infrared (NIR), the NIR quantitative phase image serves as a stable intermediate representation that is largely invariant to stain fading, which enables registration-free dataset generation and supervised training. Our prototype virtually re-stains faded stomach slide of a 3.9 x 2.6mm(2) field of view in approximately 1 minute.

This joint feature issue of Optics Express, Applied Optics, and Biomedical Optics Express showcases recent work from the Computational Optical Sensing and Imaging community. The issue reflects the continuing expansion of computational optical sensing and imaging, where optical design, detection, calibration, physical optical system modeling, signal processing, and machine learning to acquire task-relevant information. The selected papers span computational microscopy and biomedical imaging, adaptive optics, wavefront sensing, holography, phase retrieval, single-pixel and ghost imaging, hyperspectral and polarization imaging, non-line-of-sight imaging, LiDAR, photon-efficient sensing, optical-system co-design, and emerging applications in medicine, remote sensing, manufacturing, and environmental metrology. Together, these contributions illustrate how computationally co-designed optical systems are moving from algorithmic demonstrations toward robust, application-specific imaging and sensing platforms.

Hyperspectral fluorescence microscopy enables important biological and clinical applications, but conventional systems are bulky or require scanning, limiting temporal resolution and throughput. We introduce a computational snapshot hyperspectral microscope that uses compressed sensing to achieve higher spatial-spectral resolution than traditional snapshot systems. Our device is compact ( 15 cm x 6 cm x 6 cm) and easily attaches to standard fluorescence microscopes. We benchmark our system against existing snapshot methods through simulations to evaluate its spatial and spectral performance. Experimental imaging of fluorescent beads, labeled cells, and lanthanide hydrogel beads demonstrates a practical, high-throughput solution for hyperspectral microscopy in biological and clinical applications.

Diffusion models have been extensively explored for solving ill-posed inverse problems, achieving remarkable performance. However, their applicability to real-world scenarios, such as lensless imaging, has not been well investigated. Modern lensless imaging has compact form factor, low-cost hardware requirement, and intrinsic compressive imaging capabilities, but these advantages pose highly ill-posed inverse problems. In this work, we introduce a training-free zero-shot diffusion model, termed Dilack, for restoring raw images captured by lensless cameras that are degraded by large and complex kernels. Our approach incorporates novel data fidelity terms, referred to as the pseudo-inverse anchor for constraining (PiAC) fidelity loss, to enhance reconstruction quality by addressing the ill-posed nature of challenging inverse problems. Additionally, inspired by locally acting classical regularizers, we propose integrating masked fidelity within the PiAC loss. This scheme enables interaction with globally acting diffusion models while adaptively enforcing spatially and stepwise local fidelity through masks. Our proposed framework effectively mitigates erratic behavior and inherent artifacts in diffusion models when used for highly ill-posed inverse problems, significantly improving the quality of lensless camera raw image restoration, including perceptual aspects. Experimental results on both synthetic and real-world datasets for modern lensless imaging demonstrate that our approach outperforms prior arts including classical and existing diffusion based methods. The code is available at https://github.com/mundongju/Dilack.

The 2024 Optica Imaging Congress showcased the latest advances in Computational Optical Sensing and Imaging (COSI), highlighting innovations in various fields including microscopy, tomographic imaging, and computational photography. This feature issue, compiled from the meeting, includes 36 selected papers covering new imaging techniques, algorithmic breakthroughs, and machine learning-driven approaches for optical imaging and sensing. From learning-based reconstructions to hardware-accelerated imaging, these works demonstrate significant progress in efficiency, accuracy, and applicability. This collection aims to serve as a resource for researchers and inspire future developments in computational imaging and sensing.

This joint feature issue from Optics Express and Applied Optics includes papers from the Computational Optical Sensing and Imaging meeting held during the 2024 Optica Imaging Congress and Optica Sensing Congress.

Photodiodes based on two-dimensional semiconductors are of potential use in the development of optoelectronic devices, but their photovoltaic efficiency is limited by strong Fermi level pinning at metal–semiconductor contacts. Typical metal–interlayer–semiconductor contacts can address this issue, but can also lead to an increase in series resistance. Here we report a conductive-bridge interlayer contact that offers both Fermi level depinning and low resistance. We create an oxide interlayer that decouples the metal and semiconductor, while embedded gold nanoclusters in the interlayer act as conductive paths that facilitate efficient charge transport. Using these contacts, we fabricate a tungsten disulfide (WS2) photodiode with a photoresponsivity of 0.29 A W−1, linear dynamic range of 122 dB and power conversion efficiency of 9.9

We propose a compact lensless eye tracker that simultaneously extracts glint and pupil features using a polarization-multiplexed point spread function, enabling raw-domain glint localization and pupil reconstruction for accurate gaze estimation.

We propose a virtual staining framework for phase images using a decoupled encoder-decoder and edge regularization network, improving unsupervised phase-to-intensity translation for label-free histopathology with enhanced visual quality.

Fourier ptychography (FP) is widely adopted for label-free, high-resolution quantitative phase imaging (QPI) of biological samples. However, its imaging speed is limited by the need for multiple acquisitions. In this work, we propose a single-shot FP technique that uses linear polarizers to encode multiple illumination wavevectors and a polarization camera to capture multiple sets of information simultaneously. A multiplexed FP algorithm, utilizing both the bright-field and dark-field information, reconstructs a high-resolution quantitative phase image from the single-shot intensity image. Verified with resolution targets and a histological sample, our method achieved a resolution improvement of 2.5 times the diffraction limit of the objective lens and provided QPI over a large field-of-view. Additionally, we demonstrated high-speed FP at 75 frames per second, limited only by the sensor's readout speed, enabling QPI of fast-moving microorganisms.

Diffuse Correlation Spectroscopy (DCS) allows the label-free investigation of microvascular dynamics deep within living tissue. However, common implementations of DCS are currently limited to measurement depths of $\sim 1-1.5cm$, which can limit the accuracy of cerebral hemodynamics measurement. Here we present massively parallelized DCS (pDCS) using novel single photon avalanche detector (SPAD) arrays with up to 500x500 individual channels. The new SPAD array technology can boost the signal-to-noise ratio by a factor of up to 500 compared to single-pixel DCS, or by more than 15-fold compared to the most recent state-of-the-art pDCS demonstrations. Our results demonstrate the first in vivo use of this massively parallelized DCS system to measure cerebral blood flow changes at $\sim 2cm$ depth in human adults. We compared different modes of operation and applied a dual detection strategy, where a secondary SPAD array is used to simultaneously assess the superficial blood flow as a built-in reference measurement. While the blood flow in the superficial scalp tissue showed no significant change during cognitive activation, the deep pDCS measurement showed a statistically significant increase in the derived blood flow index of 8-12% when compared to the control rest state.

Cameras serve as essential tools for collecting information from the surrounding environment and providing feedback to users, yet they pose a risk to privacy. Here, we propose a method to encrypt the scene at the hardware level by designing the forward model of a lensless camera with engineered shift-variant transfer function and to decode the encrypted scene with a physics-based neural network. The masks are easily fabricated, and the reconstruction algorithm is robust across various PSF designs. The use of physics-based neural networks further enhances image reconstruction quality, making this system highly adaptable and suitable for large-scale deployment.

This joint feature issue of Optics Express and Applied Optics showcases technical innovations by participants of the 2023 topical meeting on Computational Optical Sensing and Imaging and the computational imaging community. The articles included in the feature issue highlight advances in imaging science that emphasize synergistic activities in optics, signal processing and machine learning. The issue features 26 contributed articles that cover multiple themes including non line-of-sight imaging, imaging through scattering media, compressed sensing, lensless imaging, ptychography, computational microscopy, spectroscopy and optical metrology.