Ghost Imaging Spans the Spectrum, From X-Rays to Terahertz Waves

Ghost imaging sounds like a magic trick: a camera that never looks at the object it photographs. In a typical experiment, a beam of light is split in two. One beam, the test beam, illuminates the object and is collected by a detector with no spatial resolution at all—a so-called bucket detector that simply records the total intensity of whatever light bounces back or passes through. The other beam, the reference beam, never touches the object but is measured with full spatial or temporal resolution. Neither beam alone contains an image. Yet when researchers compute the second-order correlation between the two signals, a picture of the object emerges from the statistics. A new review published in the journal Vicinagearth by Tong Tian, Sukyoon Oh and Christian Spielmann of Friedrich Schiller University Jena traces how this once-exotic quantum technique has matured into a versatile imaging platform that now spans nearly the entire electromagnetic spectrum, from extreme ultraviolet light to terahertz waves—and even to beams of atoms, neutrons and electrons.

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