Lensless Imaging With Compressive Ultrafast Sensing

Satat, Guy; Tancik, Matthew; Raskar, Ramesh

doi:10.1109/tci.2017.2684624

Cited by 56 publications

(34 citation statements)

References 38 publications

(56 reference statements)

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“…Similar to 3D imaging systems, single-pixel technology also befits the development of a potential imaging approach called lensless imaging. Figure 16b shows the typical architecture of lensless imaging systems, and no imaging and collection components are required in this case [157]. From the mentioned applications above, it should be noted that the imaging quality is highly affected by the properties of the applied optical components, especially when the system works in the non-visible range.…”

Section: Three-dimensional Imaging and Lensless Imagingmentioning

confidence: 99%

Single-Pixel MEMS Imaging Systems

Zhou

Lim

2020

Micromachines

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Single-pixel imaging technology is an attractive technology considering the increasing demand of imagers that can operate in wavelengths where traditional cameras have limited efficiency. Meanwhile, the miniaturization of imaging systems is also desired to build affordable and portable devices for field applications. Therefore, single-pixel imaging systems based on microelectromechanical systems (MEMS) is an effective solution to develop truly miniaturized imagers, owing to their ability to integrate multiple functionalities within a small device. MEMS-based single-pixel imaging systems have mainly been explored in two research directions, namely the encoding-based approach and the scanning-based approach. The scanning method utilizes a variety of MEMS scanners to scan the target scenery and has potential applications in the biological imaging field. The encoding-based system typically employs MEMS modulators and a single-pixel detector to encode the light intensities of the scenery, and the images are constructed by harvesting the power of computational technology. This has the capability to capture non-visible images and 3D images. Thus, this review discusses the two approaches in detail, and their applications are also reviewed to evaluate the efficiency and advantages in various fields.

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Section: Three-dimensional Imaging and Lensless Imagingmentioning

confidence: 99%

Single-Pixel MEMS Imaging Systems

Zhou

Lim

2020

Micromachines

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“…More recently, there has been an increased interest in using masks with appropriate computational techniques, instead of traditional lens-based cameras, to build cameras that have fewer pixels, need not be focused [12], and/or meet physical constraints [13]. All these methods are passive imagers; only very recently has the addition of an active illumination source and time-resolved sensing been proposed to reduce acquisition time in lensless systems [14].…”

Section: B Related Workmentioning

confidence: 99%

“…In order to study howf (x) relates to f (x) it is convenient to work in the Fourier domain 7 . Taking Fourier transforms of the right-hand-side expressions of both (13) and (14), and equating, it can be shown that 8…”

Section: Robustnessmentioning

confidence: 99%

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Exploiting Occlusion in Non-Line-of-Sight Active Imaging

Thrampoulidis

Shulkind

et al. 2018

IEEE Trans. Comput. Imaging

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Active non-line-of-sight imaging systems are of growing interest for diverse applications. The most commonly proposed approaches to date rely on exploiting time-resolved measurements, i.e., measuring the time it takes for short light pulses to transit the scene. This typically requires expensive, specialized, ultrafast lasers and detectors that must be carefully calibrated. We develop an alternative approach that exploits the valuable role that natural occluders in a scene play in enabling accurate and practical image formation in such settings without such hardware complexity. In particular, we demonstrate that the presence of occluders in the hidden scene can obviate the need for collecting time-resolved measurements, and develop an accompanying analysis for such systems and their generalizations. Ultimately, the results suggest the potential to develop increasingly sophisticated future systems that are able to identify and exploit diverse structural features of the environment to reconstruct scenes hidden from view.Index Terms-computational imaging, computer vision, nonline-of-sight imaging, time-of-flight cameras, LIDAR * These authors contributed equally.

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“…Sampling is performed by imprinting the sampling matrix upon the signal using a spatial light modulator (SLM). A SLM such as a digital micromirror device (DMD) [2] [3], or a liquid crystal device (LCD) [4], [5] [6], or a photonic crystal slab [7] can all be used to sample the signal. This has led to so called "lensless cameras" [4], [21] employing CS.…”

mentioning

confidence: 99%

Compressive Imaging with a Stochastic Spatial Light Modulator

Schaake¹,

Pooser²,

Jesse³

2019

Conference on Lasers and Electro-Optics

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We present a stochastic analog spatial light modulator designed for compressive imaging applications. We rely on the unpredictable nature of multi-particle collisions to provide randomization for the particle location. We demonstrate this concept in an optical imaging system using a single-pixel camera. This design can be applied to imaging or spectroscopic systems in which no analog to optical spatial light modulators currently exist or in non-optical lensless imaging systems.

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Lensless Imaging With Compressive Ultrafast Sensing

Cited by 56 publications

References 38 publications

Single-Pixel MEMS Imaging Systems

Single-Pixel MEMS Imaging Systems

Exploiting Occlusion in Non-Line-of-Sight Active Imaging

Compressive Imaging with a Stochastic Spatial Light Modulator

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