Speckle-based spectrometer

Chakrabarti, Maumita; Jakobsen, Michael Linde; Hanson, Steen Grüner

doi:10.1364/ol.40.003264

Cited by 52 publications

(32 citation statements)

References 8 publications

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“…The resultant wavemeter is demonstrated over a wide spectral operating range of approximately 600 nm in the visible and near-infrared spectrum, with wavelength resolution at the sub-femtometre level (0.3 fm measured at a centre wavelength of 780 nm). This resolution is up to two orders of magnitude better than all previous spectrometers using complex media [16,12,23]. Through the subsequent use of appropriate electronic feedback, we realize an alternative approach for laser frequency stabilization.…”

Section: Introductionmentioning

confidence: 90%

Harnessing speckle for a sub-femtometre resolved broadband wavemeter and laser stabilization

et al. 2017

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The accurate determination and control of the wavelength of light is fundamental to many fields of science. Speckle patterns resulting from the interference of multiple reflections in disordered media are well-known to scramble the information content of light by complex but linear processes. However, these patterns are, in fact, exceptionally rich in information about the illuminating source. We use a fibre-coupled integrating sphere to generate wavelength-dependent speckle patterns, in combination with algorithms based on the transmission matrix method and principal component analysis, to realize a broadband and sensitive wavemeter. We demonstrate sub-femtometre wavelength resolution at a centre wavelength of 780 nm, and a broad calibrated measurement range from 488 to 1,064 nm. This compares favourably to the performance of conventional wavemeters. Using this speckle wavemeter as part of a feedback loop, we stabilize a 780 nm diode laser to achieve a linewidth better than 1 MHz.

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Section: Introductionmentioning

confidence: 90%

Harnessing speckle for a sub-femtometre resolved broadband wavemeter and laser stabilization

et al. 2017

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“…The multispectral characteristics of speckle fields have been used successfully in a range of studies to realize speckle spectrometers [10,11,12,13,14,15]. By exploiting wavelengthdependent speckle patterns from a multimode fiber, a spectral resolution of picometers in the near-infrared and nanometers in the visible region has been demonstrated [16,17].…”

Section: Introductionmentioning

confidence: 99%

Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array

et al. 2019

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We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral transmission matrix. Optimized neural networks trained on these datasets achieve reliable reconstruction of both discrete and continuous spectra from a monochromatic camera image. Deep learning is compared to analytical inversion methods as well as to a compressive sensing algorithm and shows favourable characteristics both in the oversampling and in the sparse undersampling (compressive) regimes. The deep learning approach offers significant advantages in robustness to drift or noise and in reconstruction speed. In a proof-of-principle demonstrator we achieve real time recovery of hyperspectral information using a multi-core, multi-mode fiber array as a random scattering medium.

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“…However, in many cases, these are limited to narrow spectral ranges with a restricted spectral resolution. In recent years, a new category of spectrometers has been proposed, which exploits the natural properties of complex materials [7,8,9,10,11,12,13,14,15,16]. The use of random media in controlling information has followed our understanding that disorder provides new opportunities for highly multi-mode data processing [17,18,19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Snapshot fiber spectral imaging using speckle correlations and compressive sensing

French¹,

Gigan²,

Muskens³

2018

Opt. Express

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Snapshot spectral imaging is rapidly gaining interest for remote sensing applications. Acquiring spatial and spectral data within one image promotes fast measurement times, and reduces the need for stabilized scanning imaging systems. Many current snapshot technologies, which rely on gratings or prisms to characterize wavelength information, are difficult to reduce in size for portable hyperspectral imaging. Here, we show that a multicore multimode fiber can be used as a compact spectral imager with sub-nanometer resolution, by encoding spectral information within a monochrome CMOS camera. We characterize wavelength-dependent speckle patterns for up to 3000 fiber cores over a broad wavelength range. A clustering algorithm is employed in combination with l1-minimization to limit data collection at the acquisition stage for the reconstruction of spectral images that are sparse in the wavelength domain. We also show that in the non-compressive regime these techniques are able to accurately reconstruct broadband information.

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Speckle-based spectrometer

Cited by 52 publications

References 8 publications

Harnessing speckle for a sub-femtometre resolved broadband wavemeter and laser stabilization

Harnessing speckle for a sub-femtometre resolved broadband wavemeter and laser stabilization

Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array

Snapshot fiber spectral imaging using speckle correlations and compressive sensing

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