The new work, published in Light: Science & Applications, comes from a collaboration led by Professors Xinliang Zhang and Cheng Zhang of Huazhong University of Science and Technology, working alongside collaborators at the University of Cambridge and Kyung Hee University. Their device belongs to a class of components known as metasurfaces, which are engineered arrays of subwavelength structures that shape the wavefront of light in ways that bulk optics cannot easily achieve. In this case, the team fabricated a dielectric metasurface that acts simultaneously as an imaging lens and as an optical differentiator, a component that computes the spatial derivative of an optical field in real time. The achievement lies in doing both at once, on the same layer of material, without the cascades of lenses and processing stages that earlier designs required.

To understand why this is technically significant, it helps to consider what optical differentiation actually means. In Fourier optics, taking the derivative of an image is equivalent to multiplying its spatial frequency spectrum by a factor proportional to frequency. Conventional implementations of this operation require a pair of lenses to move between real space and Fourier space, with a filter placed at the intermediate focal plane. That architecture works, but it is bulky, alignment-sensitive and difficult to scale. Previous metasurface differentiators inherited some of these burdens, typically needing Fourier transform lens pairs or supplementary imaging optics to deliver a usable result. The new device sidesteps that complexity by engineering the point spread function of the metasurface itself, the characteristic pattern into which a point source of light is spread by the optical system, so that the desired differentiation operation is embedded directly into the imaging process.

The key innovation is spin multiplexing. Light can carry spin angular momentum in two forms, corresponding to left-handed and right-handed circular polarization. By designing the metasurface so that it responds differently to each spin state, the researchers gave a single flat optic two independent operational channels. One polarization channel performs zeroth-order differentiation, which is functionally equivalent to conventional bright-field imaging and preserves the original scene information, while the other channels execute higher-order differentiation that emphasizes high-frequency content, highlighting the regions of a scene where the light field changes rapidly, such as edges, boundaries and fine textures. The team demonstrated two versions of the device, one handling zeroth and first orders and the other handling second and third orders, all within the same single-layer architecture.

The performance figures reported by the team underline the practical maturity of the approach. The metasurface differentiators operated across a broad wavelength range extending from yellow light into the near-infrared, a span that matters for real-world applications where illumination sources vary and biological samples respond differently at different wavelengths. At the same time, the devices maintained fine spatial resolution of up to 228.0 line pairs per millimetre, corresponding to a line width of 2.19 micrometres. That combination of spectral breadth and resolving power in a single flat component is what distinguishes the work from earlier proof-of-concept demonstrations, which often achieved the computing function at the cost of imaging quality or vice versa.

To show that the platform works on real targets rather than only on synthetic test patterns, the researchers imaged two very different kinds of objects. The first was an amplitude-type object, a custom-made binary metallic pattern fabricated on a coverslip, which modulates the intensity of transmitted light. The second was a phase-type object, a transparent diatom cell, which leaves the intensity of light essentially unchanged and instead imprints information in the phase of the transmitted wavefront. Phase objects are notoriously difficult for conventional cameras to capture, because standard detectors register only intensity. The ability of the differentiator platform to extract useful contrast from such samples points toward applications in biological microscopy, where much of the structural information in unstained cells lives in phase rather than amplitude.

The contrast with conventional image processing pipelines is central to the significance of the work. In a standard architecture, an imaging system first captures the intensity of a scene, and the resulting digital images are then processed electronically, with edge detection, sharpening or feature extraction performed by algorithms running on processors. Each step in that chain costs time and energy, and the phase information of the light field is lost at the moment of detection. The all-optical differentiator instead performs the mathematical operation directly on the light field itself, before any detection takes place. The result is instantaneous multi-dimensional acquisition and processing, in which enhanced details emerge in the optical signal without any intermediate digital computation. The researchers also demonstrated that the device remains robust under high-intensity illumination, an important consideration for deployment in demanding imaging environments.

Real-time capability was validated in a living system. The team used the platform to observe live Euglena cells, showing that the metasurface can keep pace with dynamic biological samples rather than being restricted to static targets. This matters because many of the most valuable applications of computational imaging, from tracking motile microorganisms to monitoring cellular responses to stimuli, require the imaging and the computation to happen faster than the scene itself changes. An optical element that computes as it images has no inherent processing latency, so the temporal resolution of the system is set by the optics and the detector rather than by a downstream processor.

“This work provides a practical pathway toward ultracompact all-optical computing devices,” says Professor Cheng Zhang. “By embedding computation directly into the imaging process, we bypass the speed and energy bottlenecks of digital electronics while simultaneously capturing phase information that is inaccessible to conventional cameras.” The statement captures the dual promise of the approach: it addresses the throughput and power constraints that limit electronic processing, and at the same time it recovers information, namely the phase of the optical field, that a standard intensity camera simply discards.

The potential applications extend across several domains that share a common need for fast, parallel processing of optical information. Real-time biological imaging could benefit from a compact optic that highlights cellular boundaries and internal structure without staining or digital post-processing. Material inspection and machine vision systems, which depend heavily on edge and texture detection, could integrate the differentiator directly into their front-end optics, reducing the computational load on downstream electronics. Looking further ahead, the team anticipates that the same point spread function engineering strategy could be extended to implement a broader repertoire of optical computing functions, such as denoising or feature extraction, expanding the toolkit of meta-optics for intelligent imaging and sensing. As artificial intelligence continues to push against the limits of digital hardware, flat optics that compute while they image offer a glimpse of how the front end of machine vision may itself become a computational element.

Subject of Research: Spin-multiplexed point spread function engineering in dielectric metasurfaces for simultaneous optical differentiation and high-resolution imaging

Article Title: Spin-multiplexed point spread function engineering via dielectric metasurface for simultaneous optical differentiation and high-resolution imaging

Article References: Spin-multiplexed point spread function engineering via dielectric metasurface for simultaneous optical differentiation and high-resolution imaging. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: metasurface, all-optical computing, optical differentiation, point spread function, spin multiplexing, high-resolution imaging, dielectric metasurface, machine vision, biological imaging, phase imaging, nanophotonics, Light Science & Applications

The central problem the observatory addresses is one of access. The most advanced volume imaging technologies, capable of resolving cellular and subcellular architecture in three dimensions, are today concentrated in a handful of specialized laboratories around the world. Researchers who lack proximity to such facilities often cannot pursue questions that depend on high-resolution structural data, no matter how important those questions may be. NCUVO is designed to change that equation by integrating advanced high-performance electron microscopy, artificial intelligence, advanced computing and powerful data networks into a single coordinated system. The goal is a pipeline that takes a biological sample from preparation through imaging, processing and analysis, delivering interpretable datasets to investigators who study biology, medicine and the life sciences.

Leadership of the project is split between the two institutions. Neuroscientist Davi Bock, PhD, at the University of Vermont will lead and coordinate the effort, with UC San Diego Distinguished Professor of Neurosciences and Bioengineering Mark Ellisman, PhD, serving as co-principal investigator alongside Doug Taatjes, PhD, director of UVM’s Microscopy Imaging Center. Of the $19 million in new funding, approximately $15.68 million will directly support the procurement of advanced tools and the recruitment of new staff at UVM, while $3.32 million will fund the use of shared advanced imaging and high-performance computing resources at UC San Diego. The division of labor reflects the complementary strengths each campus brings to the observatory design.

Coordinated from UVM’s Larner College of Medicine, the effort will establish state-of-the-art high-throughput electron microscopy in Vermont, while UC San Diego’s National Center for Microscopy and Imaging Research, known as NCMIR, will contribute deep expertise in sample preparation and complementary advanced microscopy techniques. Data management and processing will be handled in conjunction with the San Diego Supercomputer Center, or SDSC, at UC San Diego’s Halıcıoğlu School of Data Science and Computing. Together, these three nodes form the skeleton of the observatory: instruments that image, expertise that prepares and contextualizes samples, and cyberinfrastructure that stores, curates and distributes the resulting data.

Ellisman, who directs NCMIR, framed the initiative as a deliberate shift in how the nation treats its most powerful imaging technologies. In his view, the observatory’s design is about transforming the latest sample preparation, multimodal imaging and computational technologies into a shared national capability rather than a specialty available only to a few. At NCMIR, his team will help provide the means to turn raw microscopy image data into something that can be interpreted to advance understanding of biological processes. By combining those capabilities with UVM’s new imaging instruments and SDSC’s resources for advanced cyberinfrastructure, the project aims to empower scientists around the country to complete studies that compare many samples at different levels of detail, a task that has historically been impractical outside the largest research groups.

The data challenge is enormous. Once the observatory is established, it will generate millions of gigabytes of imagery, volumes that dwarf what most laboratories can organize and analyze on their own. Guiding the project’s data curation efforts is Ilkay Altintas, PhD, chief data science officer at the San Diego Supercomputer Center and principal investigator of the NSF-funded National Data Platform. Her team will bring expertise in organizing, processing and sharing large scientific datasets, connecting the observatory’s images with NSF-supported national computing resources so that the data do not simply accumulate but become actively usable by the research community.

Altintas emphasized that the infrastructure is intended to open new windows into how living systems process information, making advanced biological imaging more accessible than ever before. Drawing on the expertise of the SDSC and the capabilities of the National Data Platform, UC San Diego will help the imaging community design and validate scalable, reproducible workflows that connect imaging data with the computing power needed to turn remarkable images into new discoveries. Reproducibility is a recurring theme in the project’s design: by standardizing how samples are prepared, imaged and processed, the observatory hopes to produce datasets that other scientists can trust, reuse and build upon.

To test whether the system can handle the diversity of real-world research, four pilot projects will exercise the observatory across different organisms, sample sizes and scientific needs. One will examine how connections in the mouse hippocampus, a brain region critical for memory, change following learning. Working with Ellisman’s team at UC San Diego and Anton Maximov’s laboratory at The Scripps Research Institute, scientists will trace circuits involving neurons and their synapses marked as active in establishing a new memory, effectively watching the structural footprint of learning take shape. Another pilot will compare retinal circuitry across species to investigate how visual systems are suited to different environments; Bock points to sharks navigating murky versus clear water as the kind of comparative question such data could address. The third and fourth projects focus on single-celled organisms, including a study of how the parasite Toxoplasma gondii crosses the blood-brain barrier to cause toxoplasmosis, a parasitic disease spread through contaminated food and cat litter.

The five-year effort is structured to culminate in a tested design, pilot datasets and an implementation plan for operating the infrastructure as a facility for the nation, a true national observatory in the tradition of shared astronomical resources but pointed inward at the machinery of life itself. Education is also a key component, with undergraduate teaching materials and training opportunities planned around the pilot data, ensuring that these new scientific resources are not merely available but that scientists know how to use them. Bock argues that the scale of the undertaking is precisely the point: there is much about the natural world that remains incompletely understood, and unlocking those secrets requires research efforts occurring at a far greater scale than any individual laboratory can manage alone.

The potential payoff extends well beyond the pilot questions. While the initial projects are expected to produce fundamental breakthroughs in basic biology, the discoveries that follow could reshape understanding of neurodegenerative diseases and, eventually, help scientists discover tomorrow’s medicines. Bock is candid about the uncertainty at the heart of the enterprise, noting that no one can predict what the observatory will find, and that unpredicted discoveries are exactly why the country has historically invested in basic research. If the NCUVO succeeds, the smallest structures of life, from the synaptic connections of a learning mouse to the invasive machinery of a single-celled parasite, will become visible to a far broader community of scientists than ever before, and the next generation of biological insight may come from anywhere in the country rather than from a privileged few institutions.

Subject of Research: Construction of a National Connectomics and Ultrastructure Volume Observatory for high-resolution 3D biological imaging

Article Title: UC San Diego and University of Vermont share $19 million from NSF to build National Biological Observatory

Article References: UC San Diego and University of Vermont share $19 million from NSF to build National Biological Observatory. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: National Science Foundation, connectomics, electron microscopy, UC San Diego, University of Vermont, NCUVO, neuroscience, San Diego Supercomputer Center, biological imaging, data curation, Toxoplasma gondii, hippocampus