Flat optics for image differentiation

Zhou, You; Zheng, Hanyu; Kravchenko, Ivan I.; Valentine, Jason

doi:10.1038/s41566-020-0591-3

Cited by 444 publications

(332 citation statements)

References 37 publications

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“…Zhou et al [61], more recently, demonstrated the applicability of flat optics for direct image differentiation using a compact all-dielectric metastructure. In their work, first a metasurface-based differentiator was used in combination with the conventional optical components and a camera sensor for high-speed edge detection (see Figure 13A).…”

Section: Resonance-based Gf Approachmentioning

confidence: 99%

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Meta-optics for spatial optical analog computing

2020

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Rapidly growing demands for high-performance computing, powerful data processing, and big data necessitate the advent of novel optical devices to perform demanding computing processes effectively. Due to its unprecedented growth in the past two decades, the field of meta-optics offers a viable solution for spatially, spectrally, and/or even temporally sculpting amplitude, phase, polarization, and/or dispersion of optical wavefronts. In this review, we discuss state-of-the-art developments, as well as emerging trends, in computational metastructures as disruptive platforms for spatial optical analog computation. Two fundamental approaches based on general concepts of spatial Fourier transformation and Green’s function (GF) are discussed in detail. Moreover, numerical investigations and experimental demonstrations of computational optical surfaces and metastructures for solving a diverse set of mathematical problems (e.g., integrodifferentiation and convolution equations) necessary for on-demand information processing (e.g., edge detection) are reviewed. Finally, we explore the current challenges and the potential resolutions in computational meta-optics followed by our perspective on future research directions and possible developments in this promising area.

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Section: Resonance-based Gf Approachmentioning

confidence: 99%

“…Two-dimensional (2D) image differentiation using nanophotonic materials[61]. (A) Schematic of a photonic crystal slab acting as a Laplacian operator that transforms an image, E in , into its second-order derivative, E out ∝ ∇ 2 E in .…”

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confidence: 99%

Meta-optics for spatial optical analog computing

2020

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“…As we revisit metasurface design besides those achievements, the ultrathin, ultralight, and flat architecture features as the core advantage of this newly developing optical design. However, only a few works focus on compact integration, 26,27 and there is a lack of systematic characterization of imaging performance. In most previous works, metalenses act only as substitutes for conventional refractive lenses and play almost the same role in conventional optical settings without showing their uniqueness for integration.…”

Section: Introductionmentioning

confidence: 99%

Metalens-integrated compact imaging devices for wide-field microscopy

Gao

et al. 2020

Adv. Photon.

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“…Edge detection captures the edge information and provides the most significant outlines of an image or an object, which has been widely applied in image processing and computer vision owning to the high processing speed and low data volume [1][2][3][4]. The most important process for edge detection is differentiation, a process generally operated by a spatial differentiator, either in a digital computation or an analog computation way [5][6][7]. Compared with digital computing, optical analog computing can process parallel information with high efficient and low power consumption, holding great potential in real-time detections [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Notably, optical spatial computing based on metamaterials including differentiators, integrators, and equation solvers is proposed by Silva et al in 2014 [34], opening new avenues for optical analog computing. Since then, various differentiator metasurfaces are developed based on photonic crystals [5,35], spin Hall effect [36], surfaces plasmons [37], high-contrast gratings [8], and Pancharatnam-Berry (PB) phase [38,39]. However, additional prisms or lenses are still required for plasmon coupling or Fourier transform in those applications [37,40], which is incompatible with the flat and compact optical systems.…”

Section: Introductionmentioning

confidence: 99%

Monolithic metasurface spatial differentiator enabled by asymmetric photonic spin-orbit interactions

Zhang

et al. 2020

Nanophotonics

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Spatial differentiator is the key element for edge detection, which is indispensable in image processing, computer vision involving image recognition, image restoration, image compression, and so on. Spatial differentiators based on metasurfaces are simpler and more compact compared with traditional bulky optical analog differentiators. However, most of them still rely on complex optical systems, leading to the degraded compactness and efficiency of the edge detection systems. To further reduce the complexity of the edge detection system, a monolithic metasurface spatial differentiator is demonstrated based on asymmetric photonic spin-orbit interactions. Edge detection can be accomplished via such a monolithic metasurface using the polarization degree. Experimental results show that the designed monolithic spatial differentiator works in a broadband range. Moreover, 2D edge detection is experimentally demonstrated by the proposed monolithic metasurface. The proposed design can be applied at visible and near-infrared wavelengths by proper dielectric materials and designs. We envision this approach may find potential applications in optical analog computing on compact optical platforms.

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Flat optics for image differentiation

Cited by 444 publications

References 37 publications

Meta-optics for spatial optical analog computing

Meta-optics for spatial optical analog computing

Metalens-integrated compact imaging devices for wide-field microscopy

Monolithic metasurface spatial differentiator enabled by asymmetric photonic spin-orbit interactions

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