Diffuse Imaging: Creating Optical Images With Unfocused Time-Resolved Illumination and Sensing

Kirmani, Ahmed; Jeelani, Haris; Montazerhodjat, Vahid; Goyal, Vivek K

doi:10.1109/lsp.2011.2174222

Cited by 21 publications

(11 citation statements)

References 7 publications

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“…Photon counting has strongly affected the field of non-line-of-sight (NLOS) imaging-i.e., the imaging of objects that are, for example, hidden behind a wall, corner, or obstacle (23)(24)(25)(26)(27)(28)(29)(30)(31)(32). Access to very high temporal resolution imaging systems allows reconstruction of a full 3D image of the hidden scene, as conceptually explained in Fig.…”

Section: Non-line-of-sight Imagingmentioning

confidence: 99%

Quantum-inspired computational imaging

Altmann

McLaughlin

Padgett

et al. 2018

Science

Self Cite

171

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Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.

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Section: Non-line-of-sight Imagingmentioning

confidence: 99%

Quantum-inspired computational imaging

Altmann

McLaughlin

Padgett

et al. 2018

Science

Self Cite

171

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show abstract

“…LIDAR was demonstrated with a compressive single pixel approach [24], [25]. Timeresolved sensing has also been suggested to recover scene reflectance [26], [27] for lensless imaging, but without the use of structured illumination and compressive sensing. Other examples of time-resolved sensing include non-line of sight imaging, for example imaging around a corner [28] and through scattering [29], [30].…”

Section: A Contributionsmentioning

confidence: 99%

Lensless Imaging With Compressive Ultrafast Sensing

Satat

Tancik

Raskar

2017

IEEE Trans. Comput. Imaging

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Lensless imaging is an important and challenging problem. One notable solution to lensless imaging is a single pixel camera which benefits from ideas central to compressive sampling. However, traditional single pixel cameras require many illumination patterns which result in a long acquisition process. Here we present a method for lensless imaging based on compressive ultrafast sensing. Each sensor acquisition is encoded with a different illumination pattern and produces a time series where time is a function of the photon's origin in the scene. Currently available hardware with picosecond time resolution enables time tagging photons as they arrive to an omnidirectional sensor. This allows lensless imaging with significantly fewer patterns compared to regular single pixel imaging. To that end, we develop a framework for designing lensless imaging systems that use ultrafast detectors. We provide an algorithm for ideal sensor placement and an algorithm for optimized active illumination patterns. We show that efficient lensless imaging is possible with ultrafast measurement and compressive sensing. This paves the way for novel imaging architectures and remote sensing in extreme situations where imaging with a lens is not possible.

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“…In general, this has been achieved using measurements of properties of the light scattered onto visible surfaces from the hidden scene. For example, these methods have relied on measurements of time-of-flight, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] coherence, [18][19][20] or even intensity-only [21][22][23][24][25][26] information. NLOS imaging using acoustic 27 and long-wave infrared 28 waves have also been recent lines of work.…”

Section: Introductionmentioning

confidence: 99%

Occlusion-based computational periscopy with consumer cameras

Murray-Bruce

Saunders

Goyal

2019

Wavelets and Sparsity XVIII

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The ability to form images of scenes hidden from direct view would be advantageous in many applications-from improved motion planning and collision avoidance in autonomous navigation to enhanced danger anticipation for first-responders in search-and-rescue missions. Recent techniques for imaging around corners have mostly relied on time-of-flight measurements of light propagation, necessitating the use of expensive, specialized optical systems. In this work, we demonstrate how to form images of hidden scenes from intensity-only measurements of the light reaching a visible surface from the hidden scene. Our approach exploits the penumbra cast by an opaque occluding object onto a visible surface. Specifically, we present a physical model that relates the measured photograph to the radiosity of the hidden scene and the visibility function due to the opaque occluder. For a given scene-occluder setup, we characterize the parts of the hidden region for which the physical model is well-conditioned for inversion-i.e., the computational field of view (CFOV) of the imaging system. This concept of CFOV is further verified through the Cramér-Rao bound of the hidden-scene estimation problem. Finally, we present a two-step computational method for recovering the occluder and the scene behind it. We demonstrate the effectiveness of the proposed method using both synthetic and experimentally measured data.

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Diffuse Imaging: Creating Optical Images With Unfocused Time-Resolved Illumination and Sensing

Cited by 21 publications

References 7 publications

Quantum-inspired computational imaging

Quantum-inspired computational imaging

Lensless Imaging With Compressive Ultrafast Sensing

Occlusion-based computational periscopy with consumer cameras

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