Optimum spectral sensitivity functions for single sensor color imaging

Sadeghipoor, Zahra; Lu, Yue M.; Süsstrunk, Sabine

doi:10.1117/12.907904

Cited by 17 publications

(9 citation statements)

References 16 publications

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“…They concluded that the use of 4 bands seems to be the optimal solution to get a good accuracy when recording color under multi-illuminants. This conclusion is shared by Sadeghipoor et al [33] who proposed an algorithm to optimize the spectral sensitivity function of the camera by finding the best demosaicing matrix for color acquisition. The constraint imposed is that the resulting function contains smooth practical filters, which are assumed to be physically realizable.…”

Section: Introductionmentioning

confidence: 70%

“…Another situation, where very wide bands are considered, does not to provide an optimal results either for demosaicing or for spectral reconstruction. After looking at the results of Sadeghipoor et al [33] and Wang et al [43], it seems that rather a relatively wide band is adequate for general demosaicing and spectral reconstruction. Indeed, we want to preserve separability between wavelengths if possible, e.g.…”

Section: Filter Considerationsmentioning

confidence: 99%

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Energy balance in Spectral Filter Array camera design

Lapray

Thomas

Gouton

et al. 2017

J. Eur. Opt. Soc.-Rapid Publ.

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Background: Multispectral imaging permits to capture more spectral information on object surface properties than color imaging. This is useful for machine vision applications. Transmittance spectral filter arrays combined with a solid state sensor form an emerging technology used for snapshot acquisition. In spectral filter arrays technology, the sensitivities of the camera have critical consequences, not only on applications, but also in the viability of the system. We discuss how to balance the energy of each channel in single exposure multispectral imaging. Methods: We propose a methodology to design filters that permits to reach an optimal balance of energy. We apply this method on practical illuminations combined with a Gaussian model of transmittance filters. Results and discussion: Our results demonstrate that we can optimize energetically the global camera response with few efforts for several cases of illumination environments, for a given sensor and a number of spectral channels. This methodology can be embedded in an application-oriented optimization framework. Conclusions: This methodology enables achieving a great range of optical design, and it can be embedded in an application-oriented optimization framework.

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Section: Introductionmentioning

confidence: 70%

Section: Filter Considerationsmentioning

confidence: 99%

Energy balance in Spectral Filter Array camera design

Lapray

Thomas

Gouton

et al. 2017

J. Eur. Opt. Soc.-Rapid Publ.

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“…The Bayer filter samples green at twice the frequency of red, which may partially explain why the SERS signal is low relative to the intensity of pixels in the dark-field image. Additionally the spectral response for the red and green channels crosses near 585 nm, 39 which is near the plasmon resonance frequency of aggregated nanoparticles. 40 The reference Ag SERS substrate is known to have broad resonances, 20 but with at least one resonance located in the spectral region that corresponds to red in the Bayer filter pattern.…”

Section: Resultsmentioning

confidence: 98%

Plasmonic color analysis of Ag-coated black-Si SERS substrate

Asiala

Marr

Gervinskas

et al. 2015

Phys. Chem. Chem. Phys.

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Red-Green-Blue (RGB) dark-field imaging can direct the choice of laser excitation for Raman enhancements on nanostructured plasmonic surfaces. Here we demonstrate that black Silicon (b-Si) is a structured surface that has been shown to effectively absorb broad wavelengths of light, but also enables surface enhanced Raman scattering (SERS) when coated with silver (Ag). Coating b-Si with increasing amounts of Ag results in increased dark-field scattering at discrete frequencies associated with localized plasmon resonances. The dark-field scattering was monitored by collecting a far-field image with an inexpensive Complementary Metal Oxide Semiconductor (CMOS) camera, similar to what is available on most mobile phones. Color analysis of the RGB pixel intensities correlates with the observed SERS intensity obtained with either green (532 nm) or red (633 nm) laser excitation in SERS experiments. Of particular note, the SERS response at 633 nm showed low spectral variation and a lack of background scattering compared to SERS at 532 nm. The difference in background suggests sub-radiant (dark or Fano resonances) may be associated with the SERS response at 633 nm and a non-resonant character of SERS. These results indicate that b-Si serves a template where Ag nucleates during physical vapor deposition. Increased deposition causes the deposits to coalesce, and at larger Ag thicknesses, bulk scattering is observed. Comparison with a high enhancement Ag SERS substrate further illustrates that a high density of plasmonic junctions, or hotspots, is important for maximizing the SERS response. The randomness of the b-Si substrate and the corresponding Ag nano-features contributes to a broadband spectral response and enhancement in SERS. Metal-coated b-Si is a promising SERS substrate due to its performance and facile fabrication.

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“…and depends on the spectral sensitivity of the sensor S ( i , j , λ ), the reflectance of the object R ( i , j , λ ), and the light source E ( i , j , λ ) which is used to illuminate the target 24 . Here ( i , j ) is the pixel location on the sensor and λ is the wavelength available at a given pixel.…”

Section: Methodsmentioning

confidence: 99%

Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling

Deglint

Kazemzadeh

Cho

et al. 2016

Sci Rep

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The simultaneous capture of imaging data at multiple wavelengths across the electromagnetic spectrum is highly challenging, requiring complex and costly multispectral image devices. In this study, we investigate the feasibility of simultaneous multispectral imaging using conventional image sensors with color filter arrays via a novel comprehensive framework for numerical demultiplexing of the color image sensor measurements. A numerical forward model characterizing the formation of sensor measurements from light spectra hitting the sensor is constructed based on a comprehensive spectral characterization of the sensor. A numerical demultiplexer is then learned via non-linear random forest modeling based on the forward model. Given the learned numerical demultiplexer, one can then demultiplex simultaneously-acquired measurements made by the color image sensor into reflectance intensities at discrete selectable wavelengths, resulting in a higher resolution reflectance spectrum. Experimental results demonstrate the feasibility of such a method for the purpose of simultaneous multispectral imaging.

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Optimum spectral sensitivity functions for single sensor color imaging

Cited by 17 publications

References 16 publications

Energy balance in Spectral Filter Array camera design

Energy balance in Spectral Filter Array camera design

Plasmonic color analysis of Ag-coated black-Si SERS substrate

Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling

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