Spectra Estimation of Fluorescent and Reflective Scenes by Using Ordinary Illuminants

Zheng, Yinqiang; Sato, Iwao; Sato, Yoichi

doi:10.1007/978-3-319-10602-1_13

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Fu et al [6] also recovered the full spectral reflectance and fluorescence spectra of scenes by using high frequency light spectra but they require a hyperspectral camera and a programmable light source: a device that can be programmed to produce arbitrary light spectra. Zheng et al [24] recovered all the different types of spectra by using 3 ordinary illuminants, but they still require a hyperspectral camera. While effective, all these methods require specialized equipment so their use in applications is limited.…”

Section: Related Workmentioning

confidence: 99%

Reflectance and Fluorescence Spectral Recovery via Actively Lit RGB Images

Lam

Sato

et al. 2016

IEEE Trans. Pattern Anal. Mach. Intell.

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In recent years, fluorescence analysis of scenes has received attention in computer vision. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescent emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescent emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

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Section: Related Workmentioning

confidence: 99%

Reflectance and Fluorescence Spectral Recovery via Actively Lit RGB Images

Lam

Sato

et al. 2016

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Fu, Lam et al [9], [11] acquire a sequence of RGB images under nine different, narrowband illuminants. They develop a multi-step algorithm that estimates the chromaticity coordinates of the reflectance and fluorescence spectra and then searches a fluorophore database arXiv:1605.04243v1 [cs.CV] 13 May 2016 Within-band ratios Reflectance, one fluorophore, spectra Fu et al [10] ∼ 30 bands Sinusoidal, 2 Within-band ratios Reflectance, one fluorophore, spectra Zheng et al [47] ∼ 30 bands Broadband, 3 Sequence of optimizations Reflectance, one fluorophore, spectra Lam et al [23] ∼ 30 bands Narrowband, ∼ 10 Multistep estimation Reflectance, one fluorophore, spectra Suo et al [37] ∼ 10 bands Narrowband, ∼ 6 Biconvex optimization Reflectance, multi fluorophore, intensity Zhang et al [46] 3 bands Broadband, 2 Ind. Component Analysis Chromaticity, intensity Fu et al [9] 3 bands Narrowband, ∼ 11 to determine the likely emission spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…To better characterize materials, it is useful to separate fluoresced photons from reflected ones, and there is widespread interest in natural and biological imaging methods that simultaneously estimate and disambiguate reflected and fluoresced photons [10], [12], [23], [37], [40], [46], [47]. In this paper we describe an algorithm for simultaneously estimating reflectance and fluorescence.…”

Section: Introductionmentioning

confidence: 99%

“…This approach requires multispectral acquisition and a spectrally controlled light source. A simpler method, requiring only a hyperspectral imager, was developed by Zheng, Sato et al [47] who proposed a compact, four parameter fluorescent spectra parameterization based on Cauchy distribution and an estimation algorithm that solves a sequence of optimizations.…”

Section: Introductionmentioning

confidence: 99%

“…We present a collection of computational separation methods to simultaneously estimate the reflectance, excitation and emission spectra of a surface. This framework builds on several prior approaches [9], [11], [37], [47]. The methods also extend the active illumination reflection estimation methods [31], [32], and the general case of fluorescence unmixing methods when surface spectral properties are unknown [2], [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous Surface Reflectance and Fluorescence Spectra Estimation

Blasinski

Farrell

Wandell

2020

IEEE Trans. on Image Process.

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There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step and we use the Alternating Direction Method of Multipliers (ADMM) approach to efficiently find a solution. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To acquire data to test the methods, we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the system and algorithm performance with the ground truth as well as with prior methods. Our approach produces lower errors compared to earlier algorithms.

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Separating Fluorescent and Reflective Components by Using a Single Hyperspectral Image

Zheng

Lam

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

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Spectra Estimation of Fluorescent and Reflective Scenes by Using Ordinary Illuminants

Cited by 6 publications

References 22 publications

Reflectance and Fluorescence Spectral Recovery via Actively Lit RGB Images

Reflectance and Fluorescence Spectral Recovery via Actively Lit RGB Images

Simultaneous Surface Reflectance and Fluorescence Spectra Estimation

Separating Fluorescent and Reflective Components by Using a Single Hyperspectral Image

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