The idea was proposed by David Klyshko in 1988 and verified experimentally in 1995 by Pittman and colleagues, who used pairs of entangled photons to form images of an object that neither photon had individually interacted with in a conventional way. For years, ghost imaging seemed inseparable from quantum entanglement. But researchers soon discovered that classical light sources—thermal lamps, polarized lasers, and laser beams passed through rotating diffusers to create pseudo-thermal speckle—could do the same job. The pivotal moment came in 2008 with computational ghost imaging, which dispensed with the reference arm entirely. Instead of measuring a reference beam, the system projects known patterns onto the object and reconstructs the image from the bucket signal alone, using the patterns as a computational reference. That simplification turned ghost imaging into a practical single-pixel imaging technology.

Mathematically, the reconstruction is straightforward. The image value at each position is obtained by correlating the bucket-detector readings with the known or measured intensity patterns, subtracting the product of the mean signals to isolate genuine fluctuations. Refinements followed quickly. Differential ghost imaging weights the correlation by the total source intensity, suppressing background noise and improving results for highly transparent or reflective objects. Normalized ghost imaging standardizes the reference intensity distribution, making the reconstruction insensitive to absolute brightness fluctuations. Compressive sensing, also introduced in 2008, exploits the fact that most natural images are sparse in some mathematical basis: by acquiring far fewer random measurements than the Nyquist sampling theorem would demand and solving a nonlinear optimization that favors sparse solutions, researchers can reconstruct images from dramatically reduced data. Deep learning entered the field in 2017, when Lyu and colleagues trained a neural network to denoise and refine low-sampling-rate ghost images, opening the door to reconstruction methods that now routinely outperform handcrafted algorithms.

Why go to all this trouble when cameras are everywhere? The answer, the review argues, is that conventional multi-pixel detectors such as CMOS and CCD sensors are optimized for narrow wavelength ranges and degrade badly outside them. In the extreme ultraviolet, at terahertz frequencies, and in much of the mid-infrared, fast high-resolution detector arrays are expensive, scarce, or simply nonexistent. A single-pixel detector, by contrast, can be chosen for peak sensitivity at almost any wavelength, and the simplicity of the setup reduces cost and complexity. Ghost imaging therefore thrives precisely where traditional imaging hardware struggles—decoupling illumination from detection and letting computation do the heavy lifting.

The technique has even crossed from photons to matter. In 2016, Khakimov and colleagues produced correlated pairs of metastable helium atoms by colliding a Bose–Einstein condensate, achieving micron-scale ghost images of a mask with massive particles. Follow-up work showed that higher-order correlations among ultracold atoms can boost image visibility without sacrificing resolution. Neutron ghost imaging, demonstrated on a reactor beamline by Kingston and colleagues, exploits the penetrating power of thermal neutrons, which pass through dense metals yet highlight light elements such as hydrogen—complementing X-rays in nondestructive materials evaluation while reducing dose. Electron ghost imaging, realized by Li and co-workers using patterned electron beams driven by a digital micromirror device, promises to cut acquisition time and specimen damage by orders of magnitude relative to conventional electron microscopy, a boon for radiation-sensitive samples.

X-ray ghost imaging may be the most consequential branch for medicine and materials science. X-rays penetrate soft tissue far more readily than bone or metal, but they also ionize, making radiation dose a critical constraint. In 2016, two groups independently achieved X-ray ghost imaging: Yu and colleagues performed lensless Fourier-transform ghost imaging with pseudo-thermal hard X-rays, while Pelliccia and colleagues split a synchrotron beam in Laue diffraction geometry and exploited natural speckle in the source. By 2018, Zhang and colleagues had demonstrated tabletop X-ray ghost imaging with dramatically reduced radiation, and a follow-up study combined Hadamard-patterned illumination with a multi-level wavelet convolutional neural network to reach roughly 10-micrometer resolution using only 18.75 percent of the Nyquist sampling rate—conditions the authors suggest could suit early-stage cancer detection. Polycapillary optics have since tripled resolution by shrinking the illumination speckle from 166 to 55 micrometers, and ghost tomography has extended the method to three-dimensional volumetric reconstruction. At free-electron lasers, ghost imaging has reached the extreme ultraviolet, enabling nanometer-scale imaging of radiation-sensitive samples.

In the visible and near-infrared, where cameras are cheap and excellent, ghost imaging finds its niche in hostile environments. Underwater, blue and violet light between 400 and 500 nanometers penetrates best, and compressive computational ghost imaging with Hadamard patterns and wavelet-domain enhancement has recovered images through turbid water; temporal ghost imaging has even carried error-free optical data across meter-scale underwater channels. Structured beams such as pseudo-Bessel rings and Lorentz beams, which self-heal after obstruction, improve contrast at depth. Deep learning has transformed the visible band: self-supervised dual networks now extract task-relevant features from raw speckle without labeled training data, block-wise networks iteratively refine multi-scale sub-images using only the bucket signal as supervision, and multi-input mutual-supervision frameworks co-train random-pattern and bucket-signal branches to correct artifacts. Multi-polarization fusion networks capture bucket signals under several polarization states and exploit their statistical differences to reconstruct scenes through dynamic scattering media.

The infrared story is equally striking. Because infrared light rides atmospheric windows, it sees through fog, smoke and dust while avoiding photo-damage—ideal for surveillance, remote sensing and diagnostics. Radwell and colleagues replaced the focal-plane array with a single indium-gallium-arsenide detector in a dual-band microscope, then pushed to 10-hertz real-time video through smoke and tinted glass, and later captured video-rate images of methane gas leaks at the 1.65-micrometer absorption line. Near-infrared ghost-imaging LiDAR has produced centimeter-resolution depth maps from a moving aircraft, and broadband single-pixel hyperspectral cameras classify concealed chemicals across 900 to 1700 nanometers at milliwatt illumination. In the mid-infrared, where digital micromirror devices fail and fast detectors are rare, Wu and colleagues used difference-frequency generation to transfer random patterns from a near-infrared signal to a mid-infrared idler between 3.2 and 4.3 micrometers, enabling ultrafast temporal ghost imaging without high-speed electronics. Graphene metasurface modulators now steer centimeter-scale beams at gigahertz rates, and passive thermal ghost imaging reconstructs hot objects in complete darkness from their self-emitted radiation.

Terahertz ghost imaging completes the spectrum. Terahertz waves pass through textiles, polymers and paper while carrying molecular-fingerprint contrast, making them attractive for security screening, pharmaceutical inspection and cultural heritage diagnostics. Chan and colleagues inaugurated the field by replacing pixelated arrays with a single photoconductive antenna and random printed masks; spinning disks, optically controlled silicon modulators, electrically tunable metamaterials and spintronic emitter arrays have since pushed acquisition toward video rate and achieved deep-sub-wavelength near-field resolution. Nonlinear ghost imaging schemes now fuse terahertz generation with time-resolved field sampling, targeting hyperspectral micro-volumetry of semitransparent samples. Water-vapor absorption, scarce modulators and heavy computational loads remain hurdles, but reconfigurable metamaterials, low-noise detectors, wavefront shaping and physics-informed neural networks are converging toward compact, real-time systems. Taken together, the review concludes, multi-wavelength ghost imaging has become a powerful low-dose alternative wherever conventional focal-plane arrays fall short—imaging with light, and with matter, in places no camera can go.

Subject of Research: Multi-wavelength ghost imaging using single-pixel correlation-based reconstruction across the electromagnetic spectrum and matter waves

Article Title: Multi-wavelength ghost imaging: a review

Article References: Tian, T., Oh, S., & Spielmann, C. (2025). Multi-wavelength ghost imaging: a review. Vicinagearth, 2(1), Article 4. https://doi.org/10.1007/s44336-025-00013-0

Image Credits: AI Generated

DOI: 10.1007/s44336-025-00013-0

Keywords: ghost imaging, single-pixel imaging, computational ghost imaging, X-ray imaging, terahertz imaging, infrared imaging, compressive sensing, deep learning, quantum imaging, matter waves, low-dose imaging, correlation imaging

The clinical motivation is straightforward. Endomicroscopy, which allows in vivo, cellular-level visualization of tissue without a physical biopsy, provides critical pathophysiological information for diagnosing gastrointestinal, pulmonary, and neurological abnormalities. Yet conventional high-resolution endoscopes depend on bulky pixelated detector arrays such as CMOS or CCD sensors, and those arrays cannot be miniaturized down to the hundred-micron scale required to reach highly constrained anatomical regions, including the deep brain, without sacrificing resolution. The result is that some of the most medically valuable imaging sites remain effectively off-limits to high-fidelity, real-time optical imaging.

Optical-fiber-based ghost imaging, also known as single-pixel imaging, has long been considered theoretically ideal for these photon-starved, size-constrained environments. Instead of a camera with millions of pixels, the approach projects a sequence of known light patterns onto a scene and records only the total reflected or transmitted intensity with a single detector. Correlating the known patterns with the measured intensities reconstructs an image computationally. In principle, the optical front end can be as simple as one fiber and one detector, which is exactly what a minimally invasive probe demands.

In practice, however, ghost imaging has been held back by several stubborn technical challenges, chief among them a fundamental speed bottleneck. Conventional implementations require slow, sequential pattern projection, either through spatial light modulators that display one pattern at a time or through wavelength sweeps that step through illumination colors sequentially. That serial approach caps the achievable frame rate and introduces severe motion artifacts whenever the target moves, which is precisely the situation in living tissue. Classical reconstruction algorithms add a second problem: substantial computational latency that makes real-time, video-rate image reconstruction impractical for clinical use.

The new system resolves both bottlenecks through a hardware-software co-design. On the hardware side, the researchers leveraged highly stabilized dual optical frequency combs, devices that generate spectra composed of many precisely spaced, phase-coherent lines. Each individual comb line is mapped to a mutually uncorrelated speckle pattern, meaning that many distinct illumination patterns are effectively generated and detected in parallel rather than one after another. Wavelength-division multiplexing, the same technology that carries many data channels through a single optical communications fiber, combines with dual-comb interferometry and ghost imaging to create a minimally invasive optical front end.

The detection step is equally elegant in its compression. After the comb light interacts with the target, the system optically measures the bucket sum of each comb-line power, essentially a hyperspectral multiply-accumulate, or MAC, operation performed in parallel across the spectral channels. In a single photodetection step, the hypercube of data containing two-dimensional spatial information is physically compressed into a one-dimensional analog temporal electrical voltage signal. All of the spatial detail survives only implicitly, encoded in how each speckle pattern weighted the light that reached the detector.

Recovering an image from such a highly compressed snapshot is fundamentally an inverse optimization problem, and this is where the software half of the co-design takes over. The team developed an application-specific deep-learning Transformer model that learns the complex correlations between the known speckle patterns and the corresponding bucket intensities of the target-encoded light. Once trained, the model reconstructs high-fidelity target images from the compressed measurements, outperforming classical reconstruction algorithms in both raw imaging accuracy and processing speed. The combination of parallel optical pattern generation and fast learned reconstruction is what pushes the system past video rates for dynamic scenes.

Dr. Myoung-Gyun Suh, Senior Scientist and Group Head at NTT Research’s Physics and Informatics Laboratories and lead author of the study, emphasized the significance of the combined approach. By merging the inherent parallelism and precision of optical frequency combs with the reconstruction capabilities of a Transformer-based deep learning model, he noted, the team eliminated the sequential projection bottleneck and significantly improved imaging speed and fidelity. He added that the hardware-software co-design dramatically simplifies the optical front end, making high-fidelity dynamic ghost imaging highly viable for size-constrained scenarios such as single-use endomicroscopic probes.

The implications extend well beyond the laboratory bench. Because the entire optical front end consists of a single-core fiber and a single-pixel detector, the architecture is naturally compatible with the narrow, flexible probes that endomicroscopy requires, and one example application highlighted by the researchers is neurosurgical endomicroscopy, where a minimally invasive optical probe must reach deep, confined anatomy. The same properties also suit portable sensing in other constrained environments where cameras and scanning optics cannot fit. By demonstrating the concept experimentally on dynamic targets, the team has moved ghost imaging a significant step toward practical deployment in real-world biomedical applications, freeing the technique from the optical table and bringing it closer to clinical neurosurgical and diagnostic use.

Subject of Research: Dual-comb ghost imaging with deep learning reconstruction for minimally invasive fiber-based endomicroscopy

Article Title: Breakthrough in optical-fiber-based minimally invasive imaging using optical frequency combs and deep learning

Article References: Breakthrough in optical-fiber-based minimally invasive imaging using optical frequency combs and deep learning. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ghost imaging, optical frequency combs, deep learning, Transformer model, endomicroscopy, single-pixel imaging, dual-comb interferometry, wavelength-division multiplexing, hyperspectral imaging, computational imaging, biomedical optics, minimally invasive imaging

Conventional hyperspectral imaging requires capturing hundreds of narrow spectral bands, and doing so quickly and efficiently has long been a bottleneck in fields ranging from biomedical diagnostics to remote sensing and industrial inspection. Cameras and spectrometers must sweep through wavelengths or use complex filters, and the resulting data volume is enormous. The new approach sidesteps much of this burden by exploiting the fact that most natural scenes are highly compressible: instead of measuring every pixel at every wavelength, the system acquires a carefully chosen set of overlapping measurements and lets a neural network do the heavy lifting of reconstruction.

Ghost imaging, the conceptual foundation of the technique, has an unusual history. First demonstrated with entangled photon pairs in the mid-1990s, it relies on the correlation between two light beams: one that interacts with the object and is measured only by a bucket detector with no spatial resolution, and another that never touches the object but is fully resolved in space. By correlating the spatial patterns of the reference beam with the total intensities recorded by the bucket detector, an image of the object emerges—even though neither detector alone ever records a conventional picture. Over the past two decades, researchers have shown that ghost imaging can work with classical thermal light, with computational patterns generated by a spatial light modulator, and with far fewer measurements than traditional imaging would suggest, a strategy known as compressive ghost imaging.

The innovation reported in this study is the marriage of that compressive framework with dual-comb illumination. Dual-comb systems generate pairs of optical frequency combs—light sources whose spectra consist of thousands of evenly spaced, phase-coherent lines. When two combs with slightly different line spacings interfere, they produce a radio-frequency signal that maps the optical spectrum onto frequencies that ordinary electronics can measure. This elegant trick underlies dual-comb spectroscopy, which has transformed the precision with which chemists and physicists can interrogate molecular fingerprints, and it has won recognition across the optics community for enabling rapid, high-resolution spectral analysis without moving parts.

In the new scheme, the dual-comb source does double duty. It provides the spectral richness needed to probe the object across many wavelengths simultaneously, while the structured patterns encoded onto the illumination supply the spatial information that ghost imaging requires. Each bucket-detector measurement therefore contains a mixture of spatial and spectral information: the total light returned by the object for one particular structured pattern, summed over the full range of comb lines. No single measurement looks like an image, and no single measurement looks like a spectrum. Only in aggregate, across the entire set of acquisitions, does the information needed to reconstruct a full hyperspectral data cube exist.

Disentangling that mixture is where deep learning enters. The researchers trained a neural network to map the stack of raw bucket measurements—essentially a long list of intensity readings—onto the corresponding hyperspectral image. Because the network learns the statistical structure of natural hyperspectral scenes, it can fill in the gaps that compressive sampling leaves behind, resolving ambiguities that would defeat simpler reconstruction algorithms. Traditional compressed-sensing methods rely on iterative optimization with hand-designed sparsity constraints, which can be slow and brittle when measurements are scarce or noisy. A trained network, by contrast, performs the reconstruction in a single forward pass, making it dramatically faster and more robust in practice.

The team validated the approach in laboratory experiments, reconstructing hyperspectral images of test targets from measurement counts far below the nominal number of pixels times spectral channels that a conventional system would require. The reconstructions preserved fine spatial detail and accurate spectral profiles across the comb’s bandwidth, confirming that the dual-comb architecture delivers genuine spectral resolution rather than coarse color information. The authors report that the deep-learning reconstruction substantially outperformed baseline compressive-sensing algorithms at the same sampling ratios, producing cleaner images with fewer artifacts and better fidelity to the ground truth.

The implications extend well beyond the laboratory bench. Hyperspectral imaging has become an indispensable tool in agriculture, where it reveals plant stress and disease before they are visible to the eye; in food safety, where it detects contamination and ripeness non-destructively; in medicine, where tissue oxygenation and pathology produce subtle spectral signatures; and in environmental monitoring, where it tracks pollutants, algal blooms, and mineral distributions from aircraft and satellites. In all of these applications, acquisition speed and light budget are critical constraints. A technique that extracts full spectral-spatial information from a sparse set of measurements could enable hyperspectral video, imaging of light-sensitive biological samples, and deployment on platforms where size, weight, and power are severely limited.

Dual-comb technology itself has been maturing rapidly, with chip-scale microresonator combs now replacing bulky mode-locked lasers in many demonstrations. The integration of combs on photonic chips suggests that the imaging architecture demonstrated here could eventually shrink to a footprint compatible with drones, endoscopes, or handheld sensors. Combined with the computational reconstruction pipeline, which runs on conventional hardware once the network is trained, the system points toward hyperspectral imaging that is simultaneously fast, compact, and information-rich—a combination that no single existing modality offers.

The work also contributes to a broader conceptual shift in optical imaging. Rather than treating measurement and computation as separate stages, modern imaging systems increasingly co-design them: the illumination pattern, the detector configuration, and the reconstruction algorithm are optimized together to maximize the information extracted per photon. Ghost imaging, once considered a curiosity of quantum optics, has proven to be a flexible canvas for this philosophy. By adding spectral dimensions through dual-comb illumination and reconstruction intelligence through deep learning, the new study illustrates how far that co-design approach can go—capturing data cubes that no camera could record directly, from measurements that no camera would ever make.

Challenges remain before the technique reaches routine use. Training the reconstruction network requires representative datasets, and performance can degrade when the imaged scenes stray far from the training distribution. The dual-comb source must maintain phase coherence and stability across the full spectral band, and the measurement time scales with the number of structured patterns projected onto the object. The researchers note, however, that the compressive framework is inherently compatible with faster spatial light modulators, single-pixel detectors with higher sensitivity, and more sophisticated network architectures, leaving substantial room for improvement on every axis of the system.

For now, the demonstration stands as a striking example of how combining mature optical technologies in unexpected ways can unlock new measurement capabilities. Ghost imaging supplies the measurement economy, dual combs supply the spectral precision, and deep learning supplies the interpretive power—three ingredients that, woven together, turn a stream of seemingly meaningless detector clicks into vivid, wavelength-resolved pictures of the world.

Subject of Research: Hyperspectral dual-comb compressive ghost imaging with deep-learning-based image reconstruction

Article Title: Hyperspectral dual-comb compressive ghost imaging with deep learning reconstruction

Article References: Suh, M.-G., Dang, D., Gao, M., Jin, Y., Shin, D.-C., Gupta, A., Park, B. J., Uzundal, C., Hu, B., Kort-Kamp, W. J. M., & Lee, H. W. H. (2026). Hyperspectral dual-comb compressive ghost imaging with deep learning reconstruction. Light: Science & Applications, 15(1), Article 380. https://doi.org/10.1038/s41377-026-02417-z

Image Credits: AI Generated

DOI: 10.1038/s41377-026-02417-z

Keywords: hyperspectral imaging, ghost imaging, dual-comb spectroscopy, deep learning, compressive sensing, optical frequency combs, single-pixel imaging, neural network reconstruction, computational imaging, Light Science & Applications, Hyperspectral, dual-comb