Using the Fingerprint Matrix to See the Unseeable

By Ankit SinghReviewed by Louis CastelDec 17 2025

Modern optical imaging struggles when light enters complex or opaque environments. However, recent research shows that a fingerprint matrix can decode scattered light to reveal hidden structures and targets. This approach captures how an object alters waves and uses that unique signature to reconstruct images in scenarios where conventional cameras fail.

Image Credit: Rawpixel.com/Shutterstock.com

What is the Fingerprint Matrix?

A fingerprint matrix is a calibrated operator that captures the unique way a target alters wave propagation as the waves travel to and from a sensor through a complex medium. It improves on the idea of reflection or transmission matrices by adding unique signatures for each object. This allows for detection and location even in challenging scenarios with lots of interference. Researchers have tested this method in both sound and light, showing it can accurately identify targets in granular materials and can be used in any situation where a reflection matrix is possible.¹

Opaque environments prevent direct line-of-sight observation because scattered and absorbed light wipes out clear images at the sensor plane. Matrix-based techniques address this by learning or estimating the system’s scattering response and using it to invert or exploit correlations in the speckle field. In biological and engineered settings, this strategy pushes imaging depths and fields of view beyond classical limits set by aberrations and memory-effect constraints.?^2,3

The Problem of Imaging Through Opaque Media

Fog, smoke, turbid fluids, and biological tissues scramble optical wavefronts, producing speckle patterns that vary with angle and position, while absorption attenuates weak ballistic components that carry straightforward image information. Traditional cameras integrate intensities without recovering phase, so they cannot unscramble wave interference without auxiliary models or measurements. Modern approaches use measured matrices and computational inversion to compensate for scattering, reconstruct objects, or even image around corners.?^4,5

The scattering matrix maps input modes of illumination to output modes at the detector and reflects the complex transformations imposed by the medium. By measuring or estimating this matrix, researchers can successfully extract hidden details or reconstruct three-dimensional images in difficult conditions, like within tissues, once the appropriate structure is applied.^3,6

How Does It Work?

Every object leaves a unique signature in the scattered wavefield, which can be captured as a fingerprint matrix derived from a reference measurement or a learned model. The method compares measured returns from a hidden target environment to the reference fingerprint and calculates likelihood maps that peak where the object’s signature best matches the data. This technique improves visibility and reliability by isolating object-specific correlations from the diffuse background imposed by the medium.?¹

To create and use fingerprint matrices, techniques like speckle analysis, wavefront shaping, and computational imaging are used. Wavefront shaping controls the incident field to excite favorable transmission channels. Speckle correlations reveal angular relationships that can be utilized for recognizing and reconstructing objects. Non-negative matrix factorization breaks down fingerprints and enables imaging beyond traditional limits without the need for guide stars or special light modulators.^2,5,7

Calibration acquires reference matrices with known targets, phantoms, or relay scenes, then uses them to generate operators that detect and localize similar targets in unknown environments. Compressive acquisition schemes and randomized illumination reduce measurement time while preserving essential matrix information. Recent studies show strong recovery when separating multiple fingerprints, demonstrating how calibrated operators can adapt to different lighting and measurement situations.^2,4

Applications: Seeing Through Walls, Fog, and More

The fingerprint matrix technique has diverse applications in fields like defense, medical diagnostics, and autonomous systems. In defense and security, non-line-of-sight (NLOS) imaging recovers hidden scenes by analyzing time-resolved returns from a relay surface. Using Bayesian methods and learned patterns, these systems can adapt to various conditions, even in unknown environments. Demonstrations show that this technology can work over kilometers, using time-of-flight measurements to track complex light paths.^4,8

In medical diagnostics, matrix-based techniques improve imaging by increasing penetration depth and restoring clarity in tissues that scatter light. For example, reflection-matrix methods have produced 3D images of the cornea, even in opaque samples, by addressing forward scattering issues. Noninvasive fluorescence imaging can now achieve wide views by separating speckle fingerprints under random illumination. The MIT Camera Culture group, led by Professor Ramesh Raskar, focuses on developing new imaging technologies to make the "invisible visible," benefiting health and sustainability, which includes technologies for seeing through tissue and around corners.^2,3,9

For autonomous vehicles and robots, NLOS techniques enhance visibility in fog, dust, and crowded urban areas. These methods help detect hidden objects and improve safety. In industrial monitoring, imaging through dirt and obstructions is vital. Ultrasound fingerprint techniques can reveal hidden targets that traditional methods might miss, allowing for safer diagnostics of machinery and structures without disassembling them.^1,4,10

Recent Breakthroughs and Commercial Technologies

Recent breakthroughs in this field include the development of advanced algorithms that can reconstruct objects from complex returns by utilizing reflection matrices, learned priors, and likelihood operators derived from data fingerprints. Additionally, time-of-flight cameras are capable of capturing transient responses for NLOS recovery, while deep learning denoisers and priors help stabilize reconstructions in low signal-to-noise ratio (SNR) conditions.^4,10

Another significant advancement reported in Nature Communications describes the use of a 3D ultrasound matrix that enhances the estimation of transmission matrices, improving aberration compensation and enabling clearer imaging through heterogeneous tissues. This development shows promising potential for transcranial applications. Moreover, noninvasive megapixel demonstrations show that scattering compensation, once tied to coherent light, can extend to fluorescence using algorithmic innovations. These advances point to clinical and industrial tools that maintain resolution and throughput without invasive interventions.?^6,11

Open literature identifies groups led by Stefan Rotter and Alexandre Aubry as pioneers of the fingerprint matrix concept with demonstrations of hidden-target detection in complex media. Companies are beginning to translate matrix-based and NLOS imaging concepts into products, with Insight Photonic Solutions known for high-speed swept lasers used in imaging and a growing patent footprint around light sources that enable advanced time-resolved modalities relevant to scattered-light reconstruction workflows.^1,12

Challenges and Limitations

Computational intensity remains significant because matrix estimation, inversion, and likelihood mapping scale with the number of input and output modes. Environmental noise and temporal decorrelation degrade the stability of measured matrices, so acquisition speed and synchronization matter for performance. Calibration can be tedious, and generalization to new targets or geometries requires robust priors or adaptive updates.?^4,5

Researchers are integrating artificial intelligence to regularize inversions, denoise measurements, and interpolate missing data, which reduces acquisition demands and improves resilience. Faster sensors and compressive strategies cut acquisition time while preserving the essential matrix structure. Acoustic and hybrid modalities complement optics, where volumetric scatter or eye-safety constraints limit traditional NLOS approaches.?^2,10

The Future of Opaque Environment Imaging

In the next five to ten years, fingerprint matrices will help create real-time video reconstructions by using fast matrix sampling and learned insights. Advances in sensors and on-chip processing will allow for handheld or vehicle-mounted devices that can see through fog, dust, and even tissue with little setup. This technology will be valuable in medical diagnostics, robotics, and infrastructure inspection.^3,4,6

Furthermore, combining this technology with augmented reality (AR) and virtual reality (VR) will allow for displays that highlight hidden hazards or anatomy during medical procedures. Spatial mapping will enhance understanding of scenes, making operations safer in low-visibility conditions. Research will focus on capturing robust matrix data in changing scenes, combining optical and acoustic information, and creating learning frameworks that adapt to different media. As more data and tools become available, fingerprint-matrix imaging will prove useful in revealing what is hidden in opaque environments.^6,8,11

References and Further Reading

1. Le Ber, A. et al. (2025). Detection and characterization of targets in complex media using fingerprint matrices. Nature Physics, 21(10), 1609-1615. DOI:10.1038/s41567-025-03016-2. https://www.nature.com/articles/s41567-025-03016-2
2. Zhu, L. et al. (2022). Large field-of-view non-invasive imaging through scattering layers using fluctuating random illumination. Nature Communications, 13, 1447. DOI:10.1038/s41467-022-29166-y. https://www.nature.com/articles/s41467-022-29166-y
3. Najar, U. et al. (2024). Harnessing forward multiple scattering for optical imaging deep inside an opaque medium. Nature Communications, 15(1), 7349. DOI:10.1038/s41467-024-51619-9. https://www.nature.com/articles/s41467-024-51619-9
4. Liu, X. et al. (2023). Non-line-of-sight imaging with arbitrary illumination and detection pattern. Nature Communications, 14(1), 3230. DOI:10.1038/s41467-023-38898-4. https://www.nature.com/articles/s41467-023-38898-4
5. Yu, Z. et al. (2022). Wavefront shaping: A versatile tool to conquer multiple scattering in multidisciplinary fields. The Innovation, 3(5), 100292. DOI:10.1016/j.xinn.2022.100292. https://www.sciencedirect.com/science/article/pii/S2666675822000881
6. Weinberg, G. et al. (2024). Noninvasive megapixel fluorescence microscopy through scattering layers by a virtual incoherent reflection matrix. Science Advances. DOI:10.1126/sciadv.adl5218. https://www.science.org/doi/10.1126/sciadv.adl5218
7. Nishizaki, Y. et al. (2024). Speckle-learning-based object recognition using optical memory effect. Optical Review, 31, 165–169. DOI:10.1007/s10043-024-00868-6. https://link.springer.com/article/10.1007/s10043-024-00868-6
8. Wu, C. et al. (2021). Non–line-of-sight imaging over 1.43 km. Proceedings of the National Academy of Sciences, 118(10), e2024468118. DOI:10.1073/pnas.2024468118. https://www.pnas.org/doi/10.1073/pnas.2024468118
9. Making the invisible visible–inside our bodies, around us, and beyond–for health, work, and connection. MIT Media Lab. https://www.media.mit.edu/groups/camera-culture/overview/
10. Li, T. et al. (2025). Ultrasound synthetic aperture non-line-of-sight imaging. Communications Physics, 8(1), 432. DOI:10.1038/s42005-025-02335-3. https://www.nature.com/articles/s42005-025-02335-3
11. Bureau, F. et al. (2023). Three-dimensional ultrasound matrix imaging. Nature Communications, 14(1), 6793. DOI:10.1038/s41467-023-42338-8. https://www.nature.com/articles/s41467-023-42338-8
12. Coherent high speed scanning lidar. (2019). Google Patents. https://patents.google.com/patent/US11366203B1/en

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