Unsupervised illuminant estimation from natural scenes: an RGB digital camera suffices

A linear pseudo-inverse method for unsupervised illuminant recovery from natural scenes is presented. The algorithm, which uses a digital RGB camera, selects the naturally occurring bright areas (not necessarily the white ones) in natural images and converts the RGB digital counts directly into the spectral power distribution of the illuminants using a learning-based spectral procedure. Computations show a good spectral and colorimetric performance when only three sensors (a three-band RGB camera) are used. These results go against previous findings concerning the recovery of spectral reflectances and radiances, which claimed that the greater the number of sensors, the better the spectral performance. Combining the device with the appropriate computations can yield spectral information about objects and illuminants simultaneously, avoiding the need for spectroradiometric measurements. The method works well and needs neither a white reference located in the natural scene nor direct measurements of the spectral power distribution of the light.     

Characterization of the human iris spectral reflectance with a multispectral imaging system

We present a multispectral system developed and optimized for measurement of the spectral reflectance and the color of the human iris. We tested several sets of filters as acquisition channels, analyzed different reconstruction algorithms, and used different samples as training sets. The results obtained show that a conventional three-channel color camera (RGB) was enough to reconstruct the analyzed reflectances with high accuracy, obtaining averaged color differences of around 2-3 CIEDE2000 units and root mean square errors of around 0.01. The device developed was used to characterize 100 real irises corresponding to 50 subjects, 68 prostheses used in clinical practice, and 17 cosmetic colored contact lenses.     

Multispectral synthesis of daylight using a commercial digital CCD camera

Performance of multispectral devices in recovering spectral data has been intensively investigated in some applications, as in spectral characterization of art paintings, but has received little attention in the context of spectral characterization of natural illumination. This study investigated the quality of the spectral estimation of daylight-type illuminants using a commercial digital CCD camera and a set of broadband colored filters. Several recovery algorithms that did not need information about spectral sensitivities of the camera sensors nor eigenvectors to describe the spectra were tested. Tests were carried out both with virtual data, using simulated camera responses, and real data obtained from real measurements. It was found that it is possible to recover daylight spectra with high spectral and colorimetric accuracy with a reduced number of three to nine spectral bands.     

Evaluation and unification of some methods for estimating reflectance spectra from RGB images

The problem of estimating spectral reflectances from the responses of a digital camera has received considerable attention recently. This problem can be cast as a regularized regression problem or as a statistical inversion problem. We discuss some previously suggested estimation methods based on critically undersampled RGB measurements and describe some relations between them. We concentrate mainly on those models that are using a priori information in the form of high-resolution measurements. We use the "kernel machine" framework in our evaluations and concentrate on the use of multiple illuminations and on the investigation of the performance of global and locally adapted estimation methods. We also introduce a nonlinear transformation of reflectance values to ensure that the estimated reflection spectra fulfill physically motivated boundary conditions. The reported experimental results are derived from measured and simulated camera responses from the Munsell Matte, NCS, and Pantone data sets.     

Optimum sensors for color constancy in scenes illuminated by daylight

The apparent color of an object within a scene depends on the spectrum of the light illuminating the object. However, recording an object's color independent of the illuminant spectrum is important in many machine vision applications. In this paper the performance of a blackbody-model-based color constancy algorithm that requires four sensors with different spectral responses is investigated under daylight illumination. In this investigation sensor noise was modeled as gaussian noise, and the responses were quantized using different numbers of bits. A projection-based algorithm whose output is invariant to illuminant is investigated to improve the results that are obtained. The performance of both of these algorithms is then improved by optimizing the spectral sensitivities of the four sensors using freely available CIE standard daylight spectra and a set of lightness-normalized Munsell reflectance data. With the optimized sensors the performance of both algorithms is shown to be comparable to the human visual system. However, results obtained with measured daylight spectra show that the standard daylights may not be sufficiently representative of measured daylight for optimization with the standard daylight to lead to a reliable set of optimum sensor characteristics.     

基于数字成像设备的光谱测量精度评价

目的构建基于数字成像设备的光谱测量系统,获得"快速、精确、低成本"的光谱反射比测量结果。方法通过数码相机获取印刷用色表ECI2002的1485个色块的3通道RGB值,根据与待测样本的相似程度对训练样本赋予不同的权重,并引入空间向量余弦度,对可以表征颜色信息的光谱反射比进行重建;同时,针对该测色系统进行镜头畸变校正、光源均匀性校正以及重复性校正。结果在训练样本数为10时,平均色差可以控制在0.62以下,均方根误差可以控制在0.01以下,运算时间可以控制在1 min内。结论构建的数字成像测量系统既可以提高测量精度,又可以提高测量效率,具有工程应用价值。     

Correcting cross-media instrument metamerism for reflectance estimation in multispectral imaging

In multispectral imaging, the color accuracy of spectral reflectance estimation degrades significantly if the medium of test samples is different from that of calibration samples. This occurs mainly for two reasons, i.e., the different characteristics of spectral reflectances and the different measurement principles between an imaging system and a spectrophotometer. In this paper, this problem is referred to as cross-media instrument metamerism. We propose to correct it by using calibration samples from a standard color chart and a limited number of tuning samples with a target medium as a priori knowledge. The reflectance transform is computed by using both calibration and tuning samples, and the metamerism transform is calculated by modeling the correlation of camera responses between neighboring imaging channels. Experimental results show that the proposed method produces satisfactory spectral and colorimetric accuracy in reflectance estimation. The method could be deployed in practical applications when the available samples of certain media are inadequate for accurate reflectance estimation in a multispectral imaging system.     

Metamer sets without spectral calibration

The set of metamers for a given device response can be calculated given the device's spectral sensitivities. Knowledge of the metamer set has been useful in practical applications such as color correction and reflectance recovery. Unfortunately, the device sensitivities of a camera or scanner are not known, and they are difficult to estimate reliably outside the laboratory. We show how metamer sets can be calculated when a device's spectral sensitivities are not known. The result is built on two observations: first, the set of all reflectance spectra consists of convex combinations of certain basic colors that tend to be very bright (or dark) and have high chroma; second, the convex combinations that describe reflectance spectra result in convex combinations of red-green-blue (RGB) values. Thus, given an RGB value, it is possible to find the set of convex combinations of the RGB values of the basic colors that generate the same RGB value. The corresponding set of convex combinations of the basic spectra is the metamer set.     

基于GA-BP神经网络的彩色扫描仪光谱特征化

为了实现彩色扫描仪的光谱特征化,采用一种GA修正的BP神经网络与PCA相结合的方法对其进行研究。首先,通过主成分分析,对训练样本的光谱反射率进行降维,以RGB信号和降维后的光谱数据作为输入、输出变量进行GA-BP神经网络的建模,对任意RGB信号都可以通过模型得到其低维光谱信号;再通过主成分分析重构光谱反射率,由此实现RGB信号对光谱反射率的重构,即实现扫描仪的光谱特征化。实验结果表明,GA的优化有效地改善了BP神经网络的极值问题,提高了模型的预测精度,PCA在不影响模型精度的同时提高了模型的效率。由此说明,所提出的模型能够满足扫描仪光谱特征化的需求。     

面向颜色再现的光谱降维方法研究

目的为了解决颜色信息光谱反射率的高维数给颜色再现过程带来的诸多问题。方法利用主流的光谱降维方法(主成分分析,Lab PQR,WSPCA,WSPCAplus),实现利于颜色再现的光谱信息筛选,从光谱精度、色度精度及变光源色差稳定性等3个方面评价这些降维方法。结果实验证明,主成分分析(PCA)法的光谱精度最高,光谱误差只有0.0076,但是在色度精度及变光源色差稳定性方面表现不好;Lab PQR法在光谱精度和色度精度间达到了很好的平衡,但是当变换光源时,其色差稳定性较差;WSPCA法去除了光源的影响,其变光源后色差变化不大,并且同时兼顾了色度精度,但以牺牲光谱精度为代价,WSPCAplus在其基础上将光谱精度提高为0.0081。结论研究结果对颜色再现过程中降维方法的选择具有一定的指导作用。     

数字图像设备色度特征化建模软件研究

彩色图像在数字图像设备之间的传输和复制过程中,为保证其色彩还原的准确性和一致性,必须实施色彩管理,而色彩管理的核心技术之一就是对数字图像设备进行色度特征化.在研究了数字图像设备色度特征化的原理和方法的基础上,基于多项式回归法,建立了数字图像的设备色度值RGB与CIE标准颜色空间色度值CIE Lab之间的非线性关系;并通过Matlab软件编程,设计一种数字图像设备色度特征化的快速建模软件.实验结果表明,应用该软件可实现设备色度值RGB与标准颜色空间色度值CIE Lab的快速精确转换.     

High-photon-throughput snapshot colour imaging using a monochromatic digital camera and a pupil-domain diffuser

Colour RGB imaging with high throughput was achieved by a monochromatic digital camera with a dispersive diffuser at its pupil. An acquired snapshot monochromatic image was converted to colour coordinates through spectra, by resorting to digital processing with a compressed sensing-based algorithm of spectral imaging. Results of optical evaluation and calibration of an optical system and colour imaging experiments are reported.     

Improvement of color reproduction in color digital holography by using spectral estimation technique

We propose a color digital holography by using spectral estimation technique to improve the color reproduction of objects. In conventional color digital holography, there is insufficient spectral information in holograms, and the color of the reconstructed images depend on only reflectances at three discrete wavelengths used in the recording of holograms. Therefore the color-composite image of the three reconstructed images is not accurate in color reproduction. However, in our proposed method, the spectral estimation technique was applied, which has been reported in multispectral imaging. According to the spectral estimation technique, the continuous spectrum of object can be estimated and the color reproduction is improved. The effectiveness of the proposed method was confirmed by a numerical simulation and an experiment, and, in the results, the average color differences are decreased from 35.81 to 7.88 and from 43.60 to 25.28, respectively.     

Spectral-reflectance reconstruction in the near-infrared region by use of conventional charge-coupled-device camera measurements

Our aim is to develop a method for obtaining the reflectance spectra of samples in the near-infrared (NIR) region (800-1,000 nm) by using a small number of measurements performed with a conventional CCD camera (multispectral imaging). We experimentally determined the spectral sensitivity of the CCD camera in the NIR range, used a method based on principal component analysis to reconstruct the spectral reflectance of the samples, and analyzed the number and shape of the filters that need tobe used to apply this method. Finally we obtained the reflectance spectra of a set of 30 spectral curves by numerical simulation. The small amount of errors in the spectral reconstruction shows the potential of this method for reconstructing spectral reflectances in the NIR range.     

基于划分子空间的数码相机颜色空间转换方法研究

基于数码相机的印刷品质量检测是未来发展的方向,在颜色检测领域需要首先解决色空间转换的精度问题。采用分子空间的多项式回归法实现了从RGB 颜色空间到CIEL* a* b* 颜色空间的转换,首先把RGB 颜色空间划分成若干个子空间,然后在每个子空间中运用最小二乘法建立多项式模型,对任意RGB 颜色值根据其所在子空间求解对应的多项式方程,即可得到L*a*b*值。实验表明,该方法的转换精度有了很大程度的提高,能够满足数码相机色空间转换的基本要求,为基于数码相机的印刷品质量检测奠定基础。     

Color image simulation for underwater optics

Underwater optical image simulation is a valuable tool for oceanic science, especially for the characterization of image processing techniques such as color restoration. In this context, simulating images with a correct color rendering is crucial. This paper presents an extension of existing image simulation models to RGB imaging. The influence of the spectral discretization of the model parameters on the color rendering of the simulated images is studied. It is especially shown that, if only RGB data of the scene chosen for simulations are available, a spectral reconstruction step prior to the simulations improves the image color rendering.     

A Bayesian Nonparametric Model for Temperature-Emissivity Separation of Long-Wave Hyperspectral Images

Long-wave infrared (LWIR) hyperspectral imagers (HSI) image a scene by collecting high-resolution spectra at each pixel. The data are similar to a camera image but have a large number of narrow spectral bands rather than the familiar broad three bands of red-green-blue in a traditional digital camera. Materials in LWIR are emissive (rather than reflective) and have unique spectra that can be used for material detection and identification. The measured spectra are a convolution of the material spectra (emissivity), the black body temperature (Planck curve), other interacting environmental spectral sources, and measurement error. One approach to material identification is temperature-emissivity separation (TES), which separates or deconvolves the material spectra from the temperature curve. To accomplish this task, we develop a unique flexible model which combines the mathematical model of the physical processes within a Bayesian nonparametric framework. In addition to offering interpretable estimates of model parameters, this model is able to identify material emissivity spectra and cluster pixels into appropriate material groups. We demonstrate our method using both a synthetic and measured dataset. The online supplementary material contains an appendix of the details of the sampling algorithm.     

A colorimetric array of metalloporphyrin derivatives for the detection of volatile organic compounds

Spin-coated layers of two metalloporphyrin derivatives (2-nitro-5,10,15,20-tetraphenylporphyrinato) zinc (II) (ZnTPP-NO₂) and (2,3-dioxo-5,10,15,20-tetraphenylporphyrinato) zinc (II) (ZnTPPO) have been used as sensing materials for the detection of volatile organic compounds (VOCs) in the UV-vis spectral range. An optical characterization in controlled atmosphere has been carried out by acquiring the absorption spectra of the two metalloporphyrin derivatives both in dry air and in the presence of different VOCs. The results of the optical VOCs sensing tests show interesting differentiation in the optical responses depending on the metal ion and the peripheral substituent of the macromolecules. A 2 × 1 colorimetric array detection of VOCs has been achieved using ZnTPP-NO₂ and ZnTPPO as immobilized on a reverse phase silica gel. The gas molecules were detected by the colorimetric array and the obtained responsive images were decomposed to RGB color components using a CMOS image sensor. RGB color components change patterns obtained from the colorimetric array give a good identification of VOCs. Morphological characterization of the sensing layer was also performed by scanning electron microscopy.     

Chromogenic sensing of biogenic amines using a chameleon probe and the red-green-blue readout of digital camera images

We report on sensing spots containing an amine reactive chromogenic probe and a green fluorescent (amine insensitive) reference dye incorporated in a hydrogel matrix on a solid support. Such spots enable rapid and direct determination of primary amines and, especially, biogenic amines (BA). A distinct color change from blue to red occurs on dipping the test spots into a pH 9.0 sample containing primary amines. BAs can be determined in the concentration range from 0.01 to 10 mM within 15 min, enabling rapid, qualitative, and semiquantitative evaluation. In the “photographic” approach, the typically 4-7.5-fold increase in fluorescence intensity of the probe at 620 nm along with the constant green fluorescence at 515 nm of a reference dye are used for quantitation of BAs. The sensing spots are photoexcited with high-power 505 nm light-emitting diodes (LEDs) in a black box. A digital picture is acquired with a commercially available digital camera, and the color information is extracted via red-green-blue (RGB) readout. The ratio of the intensities of the red (signal) channel and the green (reference) channel yields pseudocolor pictures and calibration plots.