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.

Eight-Channel Multispectral Image Database for Saliency Prediction.

Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.

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.

Evaluation of Cleaning Processes Using Colorimetric and Spectral Data for the Removal of Layers of Limewash from Medieval Plasterwork.

In the cultural heritage preservation of medieval buildings, it is common to find plaster walls covered in lime, which previously were painted in polychromy. The conservation interventions usually try to remove the whitewash, whilst maintaining the original color of the painted wall as much as possible. However, there is no agreement on which cleaning technique best preserves the original appearance of the colored plaster. Different pigments found below the lime layer may behave differently depending on the cleaning technique used. Usually, colorimetric or photometric area-based measurements are carried out to study the color of the cleaned areas to compare with their original color, obtained from pre-made plaster probes. However, this methodology fails when the mean color difference is not enough to fully characterize the changes in texture and color appearance. This study presents a set of experiments carried out using two different pigments (cinnabar and malachite) covered with lime, and treated with nine different cleaning techniques on plaster probes prepared according to medieval techniques. We have studied the effect of the cleaning process on the color and the homogeneity of the samples using a hyperspectral imaging workflow. Four different analysis methods are presented and discussed. Our results show that the proposed analysis is able to provide a much more comprehensive and diversified characterization of the quality of the cleaning method compared to the commonly used colorimetric or photometric area-based measurements.

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.

Multifocus HDR VIS/NIR hyperspectral imaging and its application to works of art.

This paper presents a complete framework for capturing and processing hyperspectral reflectance images of artworks in situ, using a hyperspectral line scanner. These capturing systems are commonly used in laboratory conditions synchronized with scanning stages specifically designed for planar surfaces. However, when the intended application domain does not allow for image capture in these controlled conditions, achieving useful spectral reflectance image data can be a very challenging task (due to uncontrolled illumination, high-dynamic range (HDR) conditions in the scene, and the influence of chromatic aberration on the image quality, among other factors). We show, for the first time, all the necessary steps in the image capturing and post-processing in order to obtain high-quality HDR-based reflectance in the visible and near infrared, directly from the data captured by using a hyperspectral line scanner coupled to a rotating tripod. Our results show that the proposed method outperforms the normal capturing process in terms of dynamic range, color and spectral accuracy. To demonstrate the potential interest of this processing strategy for on-site analysis of artworks, we applied it to the study of a vintage copy of the famous painting "Transfiguration" by Raphael, as well as a facsimile of "The Golden Haggadah" from the British Library of London. The second piece has been studied for the identification of highly reflective gold-foil covered areas.

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.

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.

Nonrigid registration with free-form deformation model of multilevel uniform cubic B-splines: application to image registration and distortion correction of spectral image cubes.

In spectral imaging, spatial and spectral information of an image scene are combined. There exist several technologies that allow the acquisition of this kind of data. Depending on the optical components used in the spectral imaging systems, misalignment between image channels can occur. Further, the projection of some systems deviates from that of a perfect optical lens system enough that a distortion of scene content in the images becomes apparent to the observer. Correcting distortion and misalignment can be complicated for spectral image data if they are different at each image channel. In this work, we propose an image registration and distortion correction scheme for spectral image cubes that is based on a free-form deformation model of uniform cubic B-splines with multilevel grid refinement. This scheme is adaptive with respect to image size, degree of misalignment, and degree of distortion, and in that sense is superior to previous approaches. We support our proposed scheme with empirical data from a Bragg-grating-based hyperspectral imager, for which a registration accuracy of approximately one pixel was achieved.

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.

Dehazing in hyperspectral images: the GRANHHADA database.

In this study, we present an analysis of dehazing techniques for hyperspectral images in outdoor scenes. The aim of our research is to compare different dehazing approaches for hyperspectral images and introduce a new hyperspectral image database called GRANHHADA (GRANada Hyperspectral HAzy Database) containing 35 scenes with various haze conditions. We conducted three experiments to assess dehazing strategies, using the Multi-Scale Convolutional Neural Network (MS-CNN) algorithm. In the first experiment, we searched for optimal triplets of spectral bands to use as input for dehazing algorithms. The results revealed that certain bands in the near-infrared range showed promise for dehazing. The second experiment involved sRGB dehazing, where we generated sRGB images from hyperspectral data and applied dehazing techniques. While this approach showed improvements in some cases, it did not consistently outperform the spectral band-based approach. In the third experiment, we proposed a novel method that involved dehazing each spectral band individually and then generating an sRGB image. This approach yielded promising results, particularly for images with a high level of atmospheric dust particles. We evaluated the quality of dehazed images using a combination of image quality metrics including reference and non-reference quality scores. Using a reduced set of bands instead of the full spectral image capture can contribute to lower processing time and yields better quality results than sRGB dehazing. If the full spectral data are available, then band-per-band dehazing is a better option than sRGB dehazing. Our findings provide insights into the effectiveness of different dehazing strategies for hyperspectral images, with implications for various applications in remote sensing and image processing.

Trichromatic red-green-blue camera used for the recovery of albedo and reflectance of rough-textured surfaces under different illumination conditions.

Photometric-stereo techniques are based on the fact that image intensity depends upon the orientation of the surface with regard to the source of the illumination and its spectral reflectance. They are of special interest when dealing with rough surfaces because they usually present shadowed regions where sudden illumination changes might be found. In the present work we introduce an extension of the four-source photometric-stereo algorithm to color images that is able to recover the surface spectral reflectance of objects captured with a red-green-blue (RGB) camera. This method allows image rendering, even for rough-textured surfaces, under different directions of the impinging illumination. In addition, the introduction of spectral recovery techniques applied to the albedo and spectral reflectance from rough surfaces offers the possibility of image rendering for scenes captured under sources of illumination differing in spectral distribution. Using albedo instead of RGB information helps to avoid any shadows or highlights that might falsify results. One of the advantages of this spectral-based photometric-stereo method is that it can recover not only the albedo values, but also the spectral reflectance spectrum of an object's surface on a pixel-by-pixel basis, as can be done with more complex hyperspectral imaging devices involving a camera coupled to an extensive set of narrowband filters.

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.

Recovering fluorescent spectra with an RGB digital camera and color filters using different matrix factorizations.

The aim of a multispectral system is to recover a spectral function at each image pixel, but when a scene is digitally imaged under a light of unknown spectral power distribution (SPD), the image pixels give incomplete information about the spectral reflectances of objects in the scene. We have analyzed how accurately the spectra of artificial fluorescent light sources can be recovered with a digital CCD camera. The red-green-blue (RGB) sensor outputs are modified by the use of successive cutoff color filters. Four algorithms for simplifying the spectra datasets are used: nonnegative matrix factorization (NMF), independent component analysis (ICA), a direct pseudoinverse method, and principal component analysis (PCA). The algorithms are tested using both simulated data and data from a real RGB digital camera. The methods are compared in terms of the minimum rank of factorization and the number of sensors required to derive acceptable spectral and colorimetric SPD estimations; the PCA results are also given for the sake of comparison. The results show that all the algorithms surpass the PCA when a reduced number of sensors is used. The experimental results suggest a significant loss of quality when more than one color filter is used, which agrees with the previous results for reflectances. Nevertheless, an RGB digital camera with or without a prefilter is found to provide good spectral and colorimetric recovery of indoor fluorescent lighting and can be used for color correction without the need of a telespectroradiometer.