Estimation of daylight spectral power distribution from uncalibrated hyperspectral radiance images.

This paper introduces a novel framework for estimating the spectral power distribution of daylight illuminants in uncalibrated hyperspectral images, particularly beneficial for drone-based applications in agriculture and forestry. The proposed method uniquely combines image-dependent plausible spectra with a database of physically possible spectra, utilizing an image-independent principal component space (PCS) for estimations. This approach effectively narrows the search space in the spectral domain and employs a random walk methodology to generate spectral candidates, which are then intersected with a pre-trained PCS to predict the illuminant. We demonstrate superior performance compared to existing statistics-based methods across various metrics, validating the framework's efficacy in accurately estimating illuminants and recovering reflectance values from radiance data. The method is validated within the spectral range of 382-1002 nm and shows potential for extension to broader spectral ranges.

Color vision deficiencies and camouflage: a comparative study between normal and CVD observers.

There is a belief that observers with color vision deficiencies (CVD) perform better in detecting camouflaged objects than normal observers. Some studies have concluded contradictory findings when studying the performance of normal and CVD observers in the camouflage detection tasks in different conditions. This work presents a literature review on this topic, dividing it into three different and contradictory types of results: better performance for CVD, for normal observers, or same performance. Besides, two psychophysical experiments have been designed and carried out in a calibrated computer monitor on both normal and CVD human observers to measure the searching times of the different types of observers needed to find camouflaged stimuli in two different types of stimuli. Results show the trend that, in our experimental conditions, normal observers need shorter searching times than CVD observers in finding camouflaged stimuli both in images of natural scenes and in images with synthetic stimuli.

Long-term effects of blue-blocking spectacle lenses on color perception.

The use of blue-blocking filters is increasing in spectacle lens users. Despite the low absorption in the blue range, some users complain about these filters because they affect their color perception. In a pilot study we have evaluated how the long-term use of 8 different blue-blocking filters impact the color perception during more than 2 weeks on a group of 18 normal color vision observers, compared with a control group of 10 observers. The evaluation was done using the FM100, the Color Assessment and Diagnosis (CAD) and an achromatic point measurement. Our results show that there is a trend to worsen with the filters on.

Band Selection for Dehazing Algorithms Applied to Hyperspectral Images in the Visible Range.

Images captured under bad weather conditions (e.g., fog, haze, mist, dust, etc.), suffer from poor contrast and visibility, and color distortions. The severity of this degradation depends on the distance, the density of the atmospheric particles and the wavelength. We analyzed eight single image dehazing algorithms representative of different strategies and originally developed for RGB images, over a database of hazy spectral images in the visible range. We carried out a brute force search to find the optimum three wavelengths according to a new combined image quality metric. The optimal triplet of monochromatic bands depends on the dehazing algorithm used and, in most cases, the different bands are quite close to each other. According to our proposed combined metric, the best method is the artificial multiple exposure image fusion (AMEF). If all wavelengths within the range 450-720 nm are used to build a sRGB renderization of the imagaes, the two best-performing methods are AMEF and the contrast limited adaptive histogram equalization (CLAHE), with very similar quality of the dehazed images. Our results show that the performance of the algorithms critically depends on the signal balance and the information present in the three channels of the input image. The capture time can be considerably shortened, and the capture device simplified by using a triplet of bands instead of the full wavelength range for dehazing purposes, although the selection of the bands must be performed specifically for a given algorithm.

Metasurface-based contact lenses for color vision deficiency: comment.

Recently Karepov and Ellenbogen [Opt. Lett.45, 1379 (2020)OPLEDP0146-959210.1364/OL.384970] claimed that a new metasurface-based contact lens is able to correct deuteranomaly. Unfortunately, their results are not supported by psychophysical experiments, and some key assumptions in their simulations were misinterpreted. All of this has led to wrong conclusions providing false expectations to the color vision deficiency community.

Spectral Filter Selection for Increasing Chromatic Diversity in CVD Subjects.

This paper analyzes, through computational simulations, which spectral filters increase the number of discernible colors (NODC) of subjects with normal color vision, as well as red-green anomalous trichromats and dichromats. The filters are selected from a set of filters in which we have modeled spectral transmittances. With the selected filters we have carried out simulations performed using the spectral reflectances captured either by a hyperspectral camera or by a spectrometer. We have also studied the effects of these filters on color coordinates. Finally, we have simulated the results of two widely used color blindness tests: Ishihara and Farnsworth-Munsell 100 Hue (FM100). In these analyses the selected filters are compared with the commercial filters from EnChroma and VINO companies. The results show that the increase in NODC with the selected filters is not relevant. The simulation results show that none of these chosen filters help color vision deficiency (CVD) subjects to pass the set of color blindness tests studied. These results obtained using standard colorimetry support the hypothesis that the use of color filters does not cause CVDs to have a perception similar to that of a normal observer.

Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range.

In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have tested and applied five single image dehazing algorithms, originally developed to work on RGB images and not requiring user interaction and/or prior knowledge about the images, on a spectral hazy image database in the visible range. We have made the evaluation using two strategies: the first is based on the analysis of eleven state-of-the-art metrics and the second is two psychophysical experiments with 126 subjects. Our results suggest that the higher the wavelength within the visible range is, the higher the quality of the dehazed images. The quality increases for low haze/fog levels. The choice of the best performing algorithm depends on the criterion prioritized by the metric design strategy. The psychophysical experiment results show that the level of agreement between observers and metrics depends on the criterion set for the observers' task.

Assessment of VINO filters for correcting red-green Color Vision Deficiency.

In our ongoing research on the effectiveness of different passive tools for aiding Color Vision Deficiency (CVD) subjects, we have analyzed the VINO 02 Amp Oxy-Iso glasses using two strategies: 1) 52 observers were studied using four color tests (recognition, arrangement, discrimination, and color-naming); 2) the spectral transmittance of the lenses were used to model the color appearance of natural scenes for different simulated CVD subjects. We have also compared VINO and EnChroma glasses. The spectral transmission of the VINO glasses significantly changed color appearance. This change would allow some CVD subjects, above all the deutan ones, to be able to pass recognition tests but not the arrangement tests. To sum up, our results support the hypothesis that glasses with filters are unable to effectively resolve the problems related to color vision deficiency.

Image processing pipeline for segmentation and material classification based on multispectral high dynamic range polarimetric images.

We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre-processing method which can be applied for the registration of HDR images after they are already built as the result of combining different low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the different polarization HDR images for each spectral band. We have focused our efforts in two main applications: object segmentation and classification into metal and dielectric classes. We have simplified the segmentation using mean shift combined with cluster averaging and region merging techniques. We compare the performance of our segmentation with that of Ncut and Watershed methods. For the classification task, we propose to use information not only in the highlight regions but also in their surrounding area, extracted from the degree of linear polarization (DoLP) maps. We present experimental results which proof that the proposed image processing pipeline outperforms previous techniques developed specifically for MSHDRPol image cubes.

Colorimetric analysis of outdoor illumination across varieties of atmospheric conditions.

Solar illumination at ground level is subject to a good deal of change in spectral and colorimetric properties. With an aim of understanding the influence of atmospheric components and phases of daylight on colorimetric specifications of downward radiation, more than 5,600,000 spectral irradiance functions of daylight, sunlight, and skylight were simulated by the radiative transfer code, SBDART [Bull. Am. Meteorol. Soc.79, 2101 (1998)], under the atmospheric conditions of clear sky without aerosol particles, clear sky with aerosol particles, and overcast sky. The interquartile range of the correlated color temperatures (CCT) for daylight indicated values from 5712 to 7757 K among the three atmospheric conditions. A minimum CCT of ∼3600 K was found for daylight when aerosol particles are present in the atmosphere. Our analysis indicated that hemispheric daylight with CCT less than 3600 K may be observed in rare conditions in which the level of aerosol is high in the atmosphere. In an atmosphere with aerosol particles, we also found that the chromaticity of daylight may shift along the green-purple direction of the Planckian locus, with a magnitude depending on the spectral extinction by aerosol particles and the amount of water vapor in the atmosphere. The data analysis showed that an extremely high value of CCT, in an atmosphere without aerosol particles, for daylight and skylight at low sun, is mainly due to the effect of Chappuis absorption band of ozone at ∼600 nm. In this paper, we compare our data with well-known observations from previous research, including the ones used by the CIE to define natural daylight illuminants.

Adaptive exposure estimation for high dynamic range imaging applied to natural scenes and daylight skies.

Digital imaging of natural scenes and optical phenomena present on them (such as shadows, twilights, and crepuscular rays) can be a very challenging task because of the range spanned by the radiances impinging on the capture system. We propose a novel method for estimating the set of exposure times (bracketing set) needed to capture the full dynamic range of a scene with high dynamic range (HDR) content. The proposed method is adaptive to scene content and to any camera response and configuration, and it works on-line since the exposure times are estimated as the capturing process is ongoing. Besides, it requires no a priori information about scene content or radiance values. The resulting bracketing sets are minimal in the default method settings, but the user can set a tolerance for the maximum percentage of pixel population that is underexposed or saturated, which allows for a higher number of shots if a better signal-to-noise ratio (SNR) in the HDR scene is desired. This method is based on the use of the camera response function that is needed for building the HDR radiance map by stitching together several differently exposed low dynamic range images of the scene. The use of HDR imaging techniques converts our digital camera into a tool for measuring the relative radiance outgoing from each point of the scene, and for each color channel. This is important for accurate characterization of optical phenomena present in the atmosphere while not suffering any loss of information due to its HDR. We have compared our method with the most similar one developed so far [IEEE Trans. Image Process.17, 1864 (2008)]. Results of the experiments carried out for 30 natural scenes show that our proposed method equals or outperforms the previously developed best approach, with less shots and shorter exposure times, thereby asserting the advantage of being adaptive to scene content for exposure time estimation. As we can also tune the balance between capturing time and the SNR in our method, we have compared its SNR performance against that of Barakat's method as well as against a ground-truth HDR image of maximum SNR. Results confirm the success of the proposed method in exploiting its tunability to achieve the desired balance of total Δt and SNR.

Evaluating logarithmic kernel for spectral reflectance estimation-effects on model parametrization, training set size, and number of sensor spectral channels.

In this work, we evaluate the conditionally positive definite logarithmic kernel in kernel-based estimation of reflectance spectra. Reflectance spectra are estimated from responses of a 12-channel multispectral imaging system. We demonstrate the performance of the logarithmic kernel in comparison with the linear and Gaussian kernel using simulated and measured camera responses for the Pantone and HKS color charts. Especially, we focus on the estimation model evaluations in case the selection of model parameters is optimized using a cross-validation technique. In experiments, it was found that the Gaussian and logarithmic kernel outperformed the linear kernel in almost all evaluation cases (training set size, response channel number) for both sets. Furthermore, the spectral and color estimation accuracies of the Gaussian and logarithmic kernel were found to be similar in several evaluation cases for real and simulated responses. However, results suggest that for a relatively small training set size, the accuracy of the logarithmic kernel can be markedly lower when compared to the Gaussian kernel. Further it was found from our data that the parameter of the logarithmic kernel could be fixed, which simplified the use of this kernel when compared with the Gaussian kernel.

Adaptive global training set selection for spectral estimation of printed inks using reflectance modeling.

The performance of learning-based spectral estimation is greatly influenced by the set of training samples selected to create the reconstruction model. Training sample selection schemes can be categorized into global and local approaches. Most of the previously proposed global training schemes aim to reduce the number of training samples, or a selection of representative samples, to maintain the generality of the training dataset. This work relates to printed ink reflectance estimation for quality assessment in in-line print inspection. We propose what we believe is a novel global training scheme that models a large population of realistic printable ink reflectances. Based on this dataset, we used a recursive top-down algorithm to reject clusters of training samples that do not enhance the performance of a linear least-square regression (pseudoinverse-based estimation) process. A set of experiments with real camera response data of a 12-channel multispectral camera system illustrate the advantages of this selection scheme over some other state-of-the-art algorithms. For our data, our method of global training sample selection outperforms other methods in terms of estimation quality and, more importantly, can quickly handle large datasets. Furthermore, we show that reflectance modeling is a reasonable, convenient tool to generate large training sets for print inspection applications.

Combining transverse field detectors and color filter arrays to improve multispectral imaging systems.

This work focuses on the improvement of a multispectral imaging sensor based on transverse field detectors (TFDs). We aimed to achieve a higher color and spectral accuracy in the estimation of spectral reflectances from sensor responses. Such an improvement was done by combining these recently developed silicon-based sensors with color filter arrays (CFAs). Consequently, we sacrificed the filter-less full spatial resolution property of TFDs to narrow down the spectrally broad sensitivities of these sensors. We designed and performed several experiments to test the influence of different design features on the estimation quality (type of sensor, tunability, interleaved polarization, use of CFAs, type of CFAs, number of shots), some of which are exclusive to TFDs. We compared systems that use a TFD with systems that use normal monochrome sensors, both combined with multispectral CFAs as well as common RGB filters present in commercial digital color cameras. Results showed that a system that combines TFDs and CFAs performs better than systems with the same type of multispectral CFA and other sensors, or even the same TFDs combined with different kinds of filters used in common imaging systems. We propose CFA+TFD-based systems with one or two shots, depending on the possibility of using longer capturing times or not. Improved TFD systems thus emerge as an interesting possibility for multispectral acquisition, which overcomes the limited accuracy found in previous studies.

Site-directed conjugation of single-stranded DNA to affinity proteins: quantifying the importance of conjugation strategy.

Affinity protein-oligonucleotide conjugates are increasingly being explored as diagnostic and therapeutic tools. Despite growing interest, these probes are typically constructed using outdated, non-selective chemistries, and little has been done to investigate how conjugation to oligonucleotides influences the function of affinity proteins. Herein, we report a novel site-selective conjugation method for furnishing affinity protein-oligonucleotide conjugates in a 93% yield within fifteen minutes. Using SPR, we explore how the choice of affinity protein, conjugation strategy, and DNA length impact target binding and reveal the deleterious effects of non-specific conjugation methods. Furthermore, we show that these adverse effects can be minimised by employing our site-selective conjugation strategy, leading to improved performance in an immuno-PCR assay. Finally, we investigate the interactions between affinity protein-oligonucleotide conjugates and live cells, demonstrating the benefits of site-selective conjugation. This work provides critical insight into the importance of conjugation strategy when constructing affinity protein-oligonucleotide conjugates.

Spectral recovery of outdoor illumination by an extension of the Bayesian inverse approach to the Gaussian mixture model.

The Bayesian inference approach to the inverse problem of spectral signal recovery has been extended to mixtures of Gaussian probability distributions of a training dataset in order to increase the efficiency of estimating the spectral signal from the response of a transformation system. Bayesian (BIC) and Akaike (AIC) information criteria were assessed in order to provide the Gaussian mixture model (GMM) with the optimum number of clusters within the spectral space. The spectra of 2600 solar illuminations measured in Granada (Spain) were recovered over the range of 360-830 nm from their corresponding tristimulus values using a linear model of basis functions, the Wiener inverse (WI) method, and the Bayesian inverse approach extended to the GMM (BGMM). A model of Gaussian mixtures for solar irradiance was deemed to be more appropriate than a single Gaussian distribution for representing the probability distribution of the solar spectral data. The results showed that the estimation performance of the BGMM method was better than either the linear model or the WI method for the spectral approximation of daylight from the three-dimensional tristimulus values.

Illuminant spectrum estimation at a pixel.

In this paper, an algorithm is proposed to estimate the spectral power distribution of a light source at a pixel. The first step of the algorithm is forming a two-dimensional illuminant invariant chromaticity space. In estimating the illuminant spectrum, generalized inverse estimation and Wiener estimation methods were applied. The chromaticity space was divided into small grids and a weight matrix was used to estimate the illuminant spectrum illuminating the pixels that fall within a grid. The algorithm was tested using a different number of sensor responses to determine the optimum number of sensors for accurate colorimetric and spectral reproduction. To investigate the performance of the algorithm realistically, the responses were multiplied with Gaussian noise and then quantized to 10 bits. The algorithm was tested with standard and measured data. Based on the results presented, the algorithm can be used with six sensors to obtain a colorimetrically good estimate of the illuminant spectrum at a pixel.

Extending "color constancy" outside the visible region.

In this paper, the results of an investigation of the possibility of extending "color constancy" to obtain illuminant-invariant reflectance features from data in the near-ultraviolet (UV) and near-infrared (IR) wavelength regions are reported. These features are obtained by extending a blackbody-model-based color constancy algorithm proposed by Ratnasingam and Collins [J. Opt. Soc. Am. A27, 286 (2010)] to these additional wavelengths. Ratnasingam and Collins applied the model-based algorithm in the visible region to extract two illuminant-invariant features related to the wavelength-dependent reflectance of a surface from the responses of four sensors. In this paper, this model-based algorithm is extended to extract two illuminant-invariant reflectance features from the responses of sensors that cover the visible and either the near-UV or near-IR wavelength. In this investigation, test reflectance data sets are generated using the goodness-fitness coefficient (GFC). The appropriateness of the GFC for generating the test data sets is demonstrated by comparing the results obtained with these data with those obtained from data sets generated using the CIELab distance. Results based upon the GFC are then presented that suggest that the model-based algorithm can extract useful features from data from the visible and near-IR wavelengths. Finally, results are presented that show that, although the spectrum of daylight in the near UV is very different from a blackbody spectrum, the algorithm can be modified to extract useful features from visible and near-UV wavelengths.

Color changes in objects in natural scenes as a function of observation distance and weather conditions.

We have analyzed the changes in the color of objects in natural scenes due to atmospheric scattering according to changes in the distance of observation. Hook-shaped curves were found in the chromaticity diagram when the object moved from zero distance to long distances, where the object chromaticity coordinates approached the color coordinates of the horizon. This trend is the result of the combined effect of attenuation in the direct light arriving to the observer from the object and the airlight added during its trajectory. Atmospheric scattering leads to a fall in the object's visibility, which is measurable as a difference in color between the object and the background (taken here to be the horizon). Focusing on color difference instead of luminance difference could produce different visibility values depending on the color tolerance used. We assessed the cone-excitation ratio constancy for several objects at different distances. Affine relationships were obtained when an object's cone excitations were represented both at zero distance and increasing distances. These results could help to explain color constancy in natural scenes for objects at different distances, a phenomenon that has been pointed out by different authors.

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.