Journal ArticleDOI
Photon-counting compressive sensing laser radar for 3D imaging
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A photon-counting, single-pixel, laser radar camera for 3D imaging where transverse spatial resolution is obtained through compressive sensing without scanning is experimentally demonstrated.Abstract:
We experimentally demonstrate a photon-counting, single-pixel, laser radar camera for 3D imaging where transverse spatial resolution is obtained through compressive sensing without scanning. We use this technique to image through partially obscuring objects, such as camouflage netting. Our implementation improves upon pixel-array based designs with a compact, resource-efficient design and highly scalable resolution.read more
Citations
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Journal ArticleDOI
Principles and prospects for single-pixel imaging
TL;DR: The working principle, advantages, technical considerations and future potential of single-pixel imaging are described, which suits a wide a variety of detector technologies.
Journal ArticleDOI
Single-pixel three-dimensional imaging with time-based depth resolution
Ming-Jie Sun,Matthew P. Edgar,Graham M. Gibson,Baoqing Sun,Neal Radwell,Robert A. Lamb,Miles J. Padgett +6 more
TL;DR: A modified time-of-flight three-dimensional imaging system, which can use compressed sensing techniques to reduce acquisition times, whilst distributing the optical illumination over the full field of view, is shown.
Journal ArticleDOI
Single-pixel imaging 12 years on: a review
TL;DR: This review considers the development of single-pixel cameras from the seminal work of Duarte et al. up to the present state of the art, covering the variety of hardware configurations, design of mask patterns and the associated reconstruction algorithms, many of which relate to the field of compressed sensing and, more recently, machine learning.
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A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging.
TL;DR: It is found that this compressive approach performs as well as other compressive sensing techniques with greatly simplified post processing, resulting in significantly faster image reconstruction, and may be useful for single-pixel imaging in the low resolution, high-frame rate regime, or video-rate acquisition.
Journal ArticleDOI
Adaptive foveated single-pixel imaging with dynamic supersampling
David B. Phillips,Ming-Jie Sun,Ming-Jie Sun,Jonathan M. Taylor,Matthew P. Edgar,Stephen M. Barnett,Graham M. Gibson,Miles J. Padgett +7 more
TL;DR: The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
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An Introduction To Compressive Sampling
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
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Compressive Sensing [Lecture Notes]
TL;DR: This lecture note presents a new method to capture and represent compressible signals at a rate significantly below the Nyquist rate, called compressive sensing, which employs nonadaptive linear projections that preserve the structure of the signal.
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Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
TL;DR: This paper proposes gradient projection algorithms for the bound-constrained quadratic programming (BCQP) formulation of these problems and test variants of this approach that select the line search parameters in different ways, including techniques based on the Barzilai-Borwein method.