{
"id": "ep5e1q44r2",
"slug": "lensless-imaging",
"title": "Lensless imaging",
"updated": "2026-10-15",
"topic_path": [
{
"id": "technology",
"label": "Technology and the built world",
"api_url": "https://www.edgepedia.org/api/v1/topics/technology"
},
{
"id": "technology.computing",
"label": "Computing and digital systems",
"api_url": "https://www.edgepedia.org/api/v1/topics/technology.computing"
}
],
"geo": [
{
"id": "geo.nongeo.t2001.technology.computing",
"label": "Non-geographic · 2001 to 2020: Computing and digital systems",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.technology.computing",
"path": [
{
"id": "geo.nongeo",
"label": "Non-geographic",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo"
},
{
"id": "geo.nongeo.t2001",
"label": "Non-geographic · 2001 to 2020",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001"
},
{
"id": "geo.nongeo.t2001.technology",
"label": "Technology and the built world",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.technology"
},
{
"id": "geo.nongeo.t2001.technology.computing",
"label": "Computing and digital systems",
"api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.technology.computing"
}
]
}
],
"excerpt": "Lensless imaging is a computational imaging method that replaces a camera's focusing lens with a thin coded mask or diffuser on a bare sensor, reconstructing the image algorithmically.",
"snippet": "Lensless imaging is a computational imaging method that replaces a camera's focusing lens with a thin coded mask or diffuser on a bare sensor, reconstructing the image algorithmically.",
"node": "technology.computing",
"markdown": "# Lensless imaging\n\nLensless imaging is a computational imaging method that replaces the focusing lens of a camera with a thin coded mask, phase element, or diffuser placed directly in front of a bare image sensor, and recovers the scene with a reconstruction algorithm. Each sensor pixel records a weighted sum of light from many scene points, so the raw capture is a multiplexed coded measurement rather than a picture; an inverse algorithm must demultiplex it into an image. The motivation is optics that are thin, light, inexpensive, and capable of a wide field of view, with uses spanning X-ray and gamma-ray astronomy, fluorescence microscopy, and remote sensing.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) • [2](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf) • [3](https://jinyangliang.com/wp-content/uploads/2020/10/liang_2020_rep._prog._phys._83_116101.pdf) • [4](https://www.annualreviews.org/content/journals/10.1146/annurev-bioeng-092515-010849)\n\n| Key fact | Value |\n|---|---|\n| Raw output | A coded, multiplexed sensor measurement; an image exists only after computational reconstruction[2](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf) • [3](https://jinyangliang.com/wp-content/uploads/2020/10/liang_2020_rep._prog._phys._83_116101.pdf) |\n| Forward model | \\( y = \\Phi x + n \\); for a 1-megapixel scene \\( \\Phi \\) has roughly \\( 10^{12} \\) elements, so separable or convolutional structure is essential[5](https://par.nsf.gov/servlets/purl/10217884) |\n| FlatCam thinness | 0.5 mm mask-to-sensor spacing on a 6.7 mm sensor, thickness-to-width ratio ≈ 0.075[2](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf) |\n| FlatScope | Less than 1 mm thick, about 0.2 g, lateral resolution below 2 μm, 6.52 mm² field of view[6](https://www.science.org/doi/10.1126/sciadv.1701548) |\n| DiffuserCam | 100 million reconstructed voxels from a single 1.3-megapixel exposure[7](https://ar5iv.labs.arxiv.org/html/1710.02134) |\n| Mask throughput | Amplitude masks block roughly half the light and lower SNR; phase masks pass most light[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) • [8](https://www.mdpi.com/2079-9292/13/3/617) |\n| Spaceflight demo | BUPT-spectra01 coded-aperture hyperspectral payload: 520 km orbit, 50 m resolution, 47 bands in 400–700 nm, 1.535 kg[9](https://www.nature.com/articles/s41377-026-02296-4) |\n\n## How it works\n\nA coded-mask camera consists of a plate of transparent and opaque elements on a regular grid above a position-sensitive detector. Photons from a given scene direction cast the mask pattern onto the sensor, shifted by a distance that uniquely encodes that direction, so the measurement is a superposition of shifted mask shadows, one per scene point.[10](https://personal.sron.nl/~jeanz/cai/coded_intr.html) • [1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) For decoding to be possible, every scene position must be encoded distinctly, a condition expressed through the mask autocorrelation, which should approximate a delta function for optimum signal-to-noise ratio.[10](https://personal.sron.nl/~jeanz/cai/coded_intr.html)\n\nMathematically the measurement is \\( y = \\Phi x + n \\), where \\( \\Phi \\) is the system matrix; when the mask aperture is small enough and the geometry is far-field, \\( \\Phi \\) reduces to a shift-invariant convolution \\( Y = P \\ast X + N \\) with the point spread function (PSF), the pattern a single point source casts on the sensor.[5](https://par.nsf.gov/servlets/purl/10217884) A bare sensor without any modulating optic gives an extremely ill-posed problem, because measurements from different scene positions are nearly identical.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) Compared with a single pinhole, a many-holed mask raises light throughput (the Jacquinot advantage) and yields greater contrast after reconstruction (the Fellgett advantage).[3](https://jinyangliang.com/wp-content/uploads/2020/10/liang_2020_rep._prog._phys._83_116101.pdf)\n\n## How it is done\n\n**Mask design.** Binary patterns based on cyclic difference sets give near-ideal delta autocorrelations.[10](https://personal.sron.nl/~jeanz/cai/coded_intr.html) Gottesman and Fenimore introduced the modified uniformly redundant array (MURA) family in Applied Optics in 1989.[11](https://doi.org/10.1364/ao.28.004344) DeWeert and Farm proposed separable Doubly-Toeplitz masks in Optical Engineering in 2015, whose separable PSFs are more robust for wide-spectrum visible imaging.[12](https://doi.org/10.1117/1.oe.54.2.023102) For phase masks, PhlatCam's Near-field Phase Retrieval (NfPR), a Gerchberg-Saxton-like iteration using Fresnel propagation, optimizes a contour-based PSF for maximal information transfer to a bit-depth-limited sensor.[13](https://doi.org/10.1109/tpami.2020.2987489)\n\n**Fabrication.** Chromium on glass or silicon wafers, patterned by photolithography and etching, is the material of choice for blocking light in visible and thermal bands; at high photon energies, binary amplitude masks were used first because lenses are impractical there.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf)\n\n**Calibration.** Under shift-invariant conditions a single PSF capture suffices; a general linear model needs on the order of \\( N^2 \\) calibration images, and 3D systems capture per-depth PSFs (DiffuserCam recorded caustic PSFs at 128 depth planes in one session).[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) • [7](https://ar5iv.labs.arxiv.org/html/1710.02134)\n\n**Reconstruction.** Fenimore and Cannon's balanced cross correlation, from their 1978 uniformly redundant array paper, cancels background contributions; classical alternatives include Wiener filtering, the maximum entropy method, and iterative removal of sources.[14](https://doi.org/10.1364/ao.17.000337) • [10](https://personal.sron.nl/~jeanz/cai/coded_intr.html) Modern methods add regularization priors (Tikhonov, total variation, compressive sensing) or learned networks.[15](https://pmc.ncbi.nlm.nih.gov/articles/PMC11927639/)\n\n## Origin\n\nThe coded aperture, in its original form a mask containing many pinholes, was devised to resolve the trade-off between spatial resolution and light throughput in a single-pinhole camera, initially for high-energy radiation before adoption at optical wavelengths.[3](https://jinyangliang.com/wp-content/uploads/2020/10/liang_2020_rep._prog._phys._83_116101.pdf) The field grew from astronomical X-ray observations, where traditional lenses are difficult to manufacture.[15](https://pmc.ncbi.nlm.nih.gov/articles/PMC11927639/) Beyond a few hundred keV, opaque mask elements must be centimeters thick, which shapes high-energy instrument design.[10](https://personal.sron.nl/~jeanz/cai/coded_intr.html) Published anchors include Fenimore and Cannon's uniformly redundant arrays (Applied Optics, 1978)[14](https://doi.org/10.1364/ao.17.000337), the Caroli and colleagues review of coded aperture imaging in X- and gamma-ray astronomy (Space Science Reviews, 1987)[16](https://doi.org/10.1007/bf00171998), and the 1989 MURA paper.[11](https://doi.org/10.1364/ao.28.004344) The modern flat-camera wave began when Asif and colleagues reported FlatCam in IEEE Transactions on Computational Imaging in 2016,[17](https://doi.org/10.1109/tci.2016.2593662) followed by Antipa and colleagues' DiffuserCam in Optica in 2017[18](https://doi.org/10.1364/optica.5.000001) and Boominathan and colleagues' PhlatCam in IEEE TPAMI in 2020.[13](https://doi.org/10.1109/tpami.2020.2987489)\n\n## Variants\n\nMasks divide into amplitude modulators, which pass or block photons, and phase modulators, subdivided into phase gratings, diffusers, and designed phase masks.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf)\n\n- **FlatCam** places a binary, 50%-transmittance separable amplitude mask about 0.5 mm from the sensor; its PSF is essentially the projected shadow of the mask, and separability makes calibration and reconstruction scalable.[2](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf) • [8](https://www.mdpi.com/2079-9292/13/3/617)\n- **FlatScope** miniaturized this to a 0.2 mm mask distance for single-frame 3D fluorescence microscopy.[6](https://www.science.org/doi/10.1126/sciadv.1701548)\n- **Fresnel zone aperture (FZA) cameras** use a zone-plate amplitude mask about 2 mm from the sensor; each point casts an FZA shadow expanded by magnification \\( 1 + z_2/z_1 \\), and one-shot compressive reconstruction with total variation removes the twin-image artifact.[19](https://www.nature.com/articles/s41377-020-0289-9) A programmable FZA displayed on an LCD adds resolution and SNR gains.[15](https://pmc.ncbi.nlm.nih.gov/articles/PMC11927639/)\n- **PhlatCam** uses a designed phase mask under 2 mm from the sensor; phase masks pass most light, giving higher SNR than amplitude masks.[13](https://doi.org/10.1109/tpami.2020.2987489)\n- **DiffuserCam** uses an off-the-shelf engineered diffuser whose pseudo-random caustics encode each volume point uniquely.[7](https://ar5iv.labs.arxiv.org/html/1710.02134)\n- **Lenslet masks** such as ConvRML's random multi-focal lenslets produce lower-multiplexing, high-contrast PSFs of multiple focal spots with minimal diffuse background.[20](https://doi.org/10.1364/oe.608614)\n- **COACH and I-COACH** combine coded apertures with incoherent digital holography, the interferenceless variant exploiting 3D location information in the diffracted object wave without two-beam self-interference.[21](https://www.mdpi.com/2227-7080/13/5/210)\n- A lensless compound eye microsystem for real-time target motion perception was reported by Zhang and colleagues in Microsystems & Nanoengineering in 2022.[22](https://doi.org/10.1038/s41378-022-00388-w)\n\n## Applications\n\n[Coded aperture imaging](https://www.edgepedia.org/coded-aperture-imaging) remains standard in X-ray and gamma-ray astronomy, its original domain, where lenses cannot work and thick masks are acceptable.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) • [16](https://doi.org/10.1007/bf00171998) In Earth observation, the BUPT-spectra01 payload, launched from Jiuquan Satellite Launch Center, is described as the first computational-imaging-enabled compact spaceborne snapshot compressive hyperspectral instrument: 1.535 kg in a 520 km sun-synchronous orbit, with 50 m spatial resolution and 47 bands from a single 1 ms exposure, using a binary coded aperture mask of 1500 × 1500 pixels. It is a coded-aperture compressive system rather than a strictly lensless camera, so it illustrates the family's spaceflight reach rather than a pure lensless deployment.[9](https://www.nature.com/articles/s41377-026-02296-4) For picosatellites, a TU Delft design study for [PocketQube](https://www.edgepedia.org/pocketqube) spacecraft found a lensless separable-mask camera's acceptance angle reduced by 38% horizontally and 31.8% vertically versus a lens-based system, quantifying the image-quality cost of the thin format.[23](https://repository.tudelft.nl/file/File_a579381e-a10e-41af-a3f9-a2e90d254d8d) Biomedical fluorescence microscopy is the other major application area.[6](https://www.science.org/doi/10.1126/sciadv.1701548) • [4](https://www.annualreviews.org/content/journals/10.1146/annurev-bioeng-092515-010849)\n\n## Limitations and alternatives\n\n**Noise and throughput.** Linear demultiplexing amplifies noise, especially at high spatial frequencies.[2](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf) [Amplitude](https://www.edgepedia.org/amplitude) masks lose many photons, causing low SNR that is especially problematic in fluorescence, and decoding compounds it; phase masks address this by not blocking light.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) • [8](https://www.mdpi.com/2079-9292/13/3/617)\n\n**Artifacts and calibration.** Backpropagation-style FZA reconstruction produces twin-image artifacts, removable by total-variation compressive sensing.[19](https://www.nature.com/articles/s41377-020-0289-9) [Reconstruction](https://www.edgepedia.org/reconstruction) quality depends strongly on accurate prior physical knowledge, limiting generalization across scenarios.[24](https://www.sciencedirect.com/science/article/pii/S2667325824001328) In fluorescence, up to 64% of FlatScope's sensor dynamic range was lost to filter autofluorescence.[6](https://www.science.org/doi/10.1126/sciadv.1701548)\n\n**Design trade-offs.** Smaller mask features allow a smaller mask-sensor distance and wider angular field of view but worse resolution; larger features are easier to fabricate and more tolerant of misalignment, and angular resolution falls as the mask moves closer to the sensor.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) • [2](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf) Reconstruction also adds power consumption and computational complexity, often preventing real-time viewing.[1](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf) Against alternatives, microlens-based compound-eye cameras such as TOMBO reduce thickness but offer at most a four-fold reduction, far less than coded-mask designs.[2](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf)\n\n**Reconstruction algorithms.** These fall into two classes: model-based optimization methods that solve an inverse problem with priors, and deep learning methods that learn the inverse mapping and can perform real-time inference on large-scale data.[24](https://www.sciencedirect.com/science/article/pii/S2667325824001328) FlatNet, a two-stage non-iterative network with a trainable camera-inversion stage followed by U-Net perceptual enhancement, reported orders-of-magnitude image-quality improvement over iterative optimization.[5](https://par.nsf.gov/servlets/purl/10217884) • [25](https://doi.org/10.1109/tpami.2020.3033882) Monakhova and colleagues reported learned reconstructions for practical mask-based lensless imaging in [Optics Express](https://www.edgepedia.org/optics-express) in 2019,[26](https://doi.org/10.1364/oe.27.028075) and Kingshott and colleagues developed unrolled primal-dual networks in Optics Express in 2022.[27](https://doi.org/10.1364/oe.475521) Deep networks carry a heavy computing burden, long training time, and poor adaptation to other systems or changing environments,[28](https://www.frontiersin.org/journals/imaging/articles/10.3389/fimag.2024.1336829/full) and PSF-specific models require new paired data and retraining whenever the mask changes.\n\n## References\n\n1. [Recent Advances in Lensless Imaging (Boominathan et al., Optica 2022)](https://escholarship.org/content/qt9mz4746q/qt9mz4746q_noSplash_cf760ae147f01bbe680b232f83b08434.pdf)\n2. [FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation](https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf)\n3. [Punching holes in light: recent progress in single-shot coded-aperture optical imaging (Reports on Progress in Physics)](https://jinyangliang.com/wp-content/uploads/2020/10/liang_2020_rep._prog._phys._83_116101.pdf)\n4. [Lensless Imaging and Sensing (Annual Review of Biomedical Engineering)](https://www.annualreviews.org/content/journals/10.1146/annurev-bioeng-092515-010849)\n5. [FlatNet: Deep Learning-Based Reconstruction for Mask-Based Lensless Cameras](https://par.nsf.gov/servlets/purl/10217884)\n6. [Single-frame 3D fluorescence microscopy with ultraminiature lensless FlatScope (Science Advances)](https://www.science.org/doi/10.1126/sciadv.1701548)\n7. [DiffuserCam: Lensless Single-exposure 3D Imaging (Antipa, Kuo, et al.)](https://ar5iv.labs.arxiv.org/html/1710.02134)\n8. [Advances in Mask-Modulated Lensless Imaging (Electronics, 2024)](https://www.mdpi.com/2079-9292/13/3/617)\n9. [Spaceborne snapshot compressive hyperspectral imaging (Light: Science & Applications, 2026)](https://www.nature.com/articles/s41377-026-02296-4)\n10. [Coded aperture camera imaging concept (SRON)](https://personal.sron.nl/~jeanz/cai/coded_intr.html)\n11. [Stephen R. Gottesman, E. E. Fenimore (1989). New family of binary arrays for coded aperture imaging. Applied Optics.](https://doi.org/10.1364/ao.28.004344)\n12. [Michael J. DeWeert, Brian P. Farm (2015). Lensless coded-aperture imaging with separable Doubly-Toeplitz masks. Optical Engineering.](https://doi.org/10.1117/1.oe.54.2.023102)\n13. [Vivek Boominathan and colleagues (2020). PhlatCam: Designed Phase-Mask Based Thin Lensless Camera. IEEE Transactions on Pattern Analysis and Machine Intelligence.](https://doi.org/10.1109/tpami.2020.2987489)\n14. [E. E. Fenimore, T. M. Cannon (1978). Coded aperture imaging with uniformly redundant arrays. Applied Optics.](https://doi.org/10.1364/ao.17.000337)\n15. [Lensless imaging with a programmable Fresnel zone aperture (2025)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11927639/)\n16. [E. Caroli and colleagues (1987). Coded aperture imaging in X- and gamma-ray astronomy. Space Science Reviews.](https://doi.org/10.1007/bf00171998)\n17. [M. Salman Asif and colleagues (2016). FlatCam: Thin, Lensless Cameras Using Coded Aperture and Computation. IEEE Transactions on Computational Imaging.](https://doi.org/10.1109/tci.2016.2593662)\n18. [Nick Antipa and colleagues (2017). DiffuserCam: lensless single-exposure 3D imaging. Optica.](https://doi.org/10.1364/optica.5.000001)\n19. [Single-shot lensless imaging with Fresnel zone aperture and incoherent illumination (Light: Science & Applications, 2020)](https://www.nature.com/articles/s41377-020-0289-9)\n20. [Leyla A. Kabuli and colleagues (2026). ConvRML: high-quality lensless imaging with random multi-focal lenslets. Optics Express.](https://doi.org/10.1364/oe.608614)\n21. [Recent Advances in Spatially Incoherent Coded Aperture Imaging Technologies (Technologies)](https://www.mdpi.com/2227-7080/13/5/210)\n22. [Li Zhang and colleagues (2022). A wide-field and high-resolution lensless compound eye microsystem for real-time target motion perception. Microsystems & Nanoengineering.](https://doi.org/10.1038/s41378-022-00388-w)\n23. [Lensless camera for picosatellites (TU Delft thesis, Delfi-PQ)](https://repository.tudelft.nl/file/File_a579381e-a10e-41af-a3f9-a2e90d254d8d)\n24. [Lensless camera: Unraveling the breakthroughs and prospects](https://www.sciencedirect.com/science/article/pii/S2667325824001328)\n25. [Salman Siddique Khan and colleagues (2020). FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements. IEEE Transactions on Pattern Analysis and Machine Intelligence.](https://doi.org/10.1109/tpami.2020.3033882)\n26. [Kristina Monakhova and colleagues (2019). Learned reconstructions for practical mask-based lensless imaging. Optics Express.](https://doi.org/10.1364/oe.27.028075)\n27. [Oliver Kingshott and colleagues (2022). Unrolled primal-dual networks for lensless cameras. Optics Express.](https://doi.org/10.1364/oe.475521)\n28. [Computational optical imaging: challenges, opportunities, new trends, and emerging applications (Frontiers in Imaging)](https://www.frontiersin.org/journals/imaging/articles/10.3389/fimag.2024.1336829/full)\n\n---\n*Topic: Encyclopedia › Technology and the built world › Computing and digital systems*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
"same_as": [
"https://www.ece.rice.edu/~av21/Documents/2015/FlatCam.pdf"
],
"url": "https://www.edgepedia.org/lensless-imaging",
"markdown_url": "https://www.edgepedia.org/lensless-imaging.md",
"license": {
"name": "Edgepedia Community License 1.0",
"url": "https://www.edgepedia.org/edgepedia/license",
"summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.",
"spdx": "LicenseRef-Edgepedia-Community-1.0"
},
"credit": "\"Lensless imaging\", Edgepedia (EdgeChat), https://www.edgepedia.org/lensless-imaging. Edgepedia Community License 1.0.",
"credit_md": "\"[Lensless imaging](https://www.edgepedia.org/lensless-imaging)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/lensless-imaging](https://www.edgepedia.org/lensless-imaging). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).",
"credit_html": "\"Lensless imaging\", Edgepedia (EdgeChat), https://www.edgepedia.org/lensless-imaging. Edgepedia Community License 1.0.",
"speakable": "Lensless imaging is a computational imaging method that replaces a camera's focusing lens with a thin coded mask or diffuser on a bare sensor, reconstructing the image algorithmically."
}