Sensor simulation using a spectrum tunable LED system.

This study developed a method to simulate the sensor responses and verify the effectiveness on spectral reconstruction by a spectrum tunable LED system. Studies have shown that the spectral reconstruction accuracy could be improved by including multiple channels in a digital camera. However, the real sensors with designed spectral sensitivities were hard to manufacture and validate. Therefore, the presence of a quick and reliable validation mechanism was preferred when performing evaluation. In this study, two novel approaches, i.e., channel-first and illumination-first simulations, were proposed to replicate the designed sensors with the use of a monochrome camera and a spectrum-tunable LED illumination system. In the channel-first method, the spectral sensitivities of three extra sensor channels were optimized theoretically for an RGB camera and then simulated by matching the corresponding illuminants in the LED system. The illumination-first method optimized the spectral power distribution (SPD) of the lights using the LED system, and the extra channels could be determined accordingly. The results of practical experiments showed that the proposed methods were effective to simulate the responses of the extra sensor channels.

Optimized principal component analysis for camera spectral sensitivity estimation.

This paper describes the use of a weighted principal component analysis (PCA) method for camera spectral sensitivity estimation. A comprehensive set of spectral sensitivities of 111 cameras was collected from four publicly available databases. It was proposed to weight the spectral sensitivities in the database according to the similarities with those of the test camera. The similarity was evaluated by the reciprocal predicted errors of camera responses. Thus, a set of dynamic principal components was generated from the weighted spectral sensitivity database and served as the basis functions to estimate spectral sensitivities. The test stimuli included self-luminous colors from a multi-channel LED system and reflective colors from a color chart. The proposed method was tested in both the simulated and practical experiments, and the results were compared with the classical PCA method, three commonly used basis function methods (Fourier, polynomial, and radial bases), and a regularization method. It was demonstrated that the proposed method significantly improved the accuracy of spectral sensitivity estimation.

Estimation of the perceptual color gamut on displays.

A new method was developed to evaluate the perceived gamut of a display. A multispectral image of a white gypsum sphere was projected onto various highly chromatic lights, producing a series of images with distinct hue perceptions at the gamut boundary of displays. These images were subsequently used in a psychophysical experiment to examine the perceived color gamut. Afterwards, the visual results were further compared with the prediction results from various uniform color spaces (UCSs) and color appearance models (CAMs). The present results demonstrate that CAM16-UCS provides the most accurate prediction across the entire color gamut, whereas the cyan-to-blue region is more poorly predicted than the other hue regions for all CAMs and UCSs investigated.

Personalized image enhancement method for color deficient observers.

A personalized image enhancement method is proposed to improve color vision in hereditary color vision deficiency (CVD). It is divided into two stages: evaluation of CVD and gamut mapping for image enhancement. These two separate stages are connected via a psychophysical experiment, through which the deficiency test result expressed using the C-index can be further transformed into a physical parameter, namely the wavelength shift of the cone fundamental. Experiments conducted by the color-deficient observers (CDOs) validated this proposed method, and it is emphasized that the proposed method is just serving as a template for image enhancement. A more advanced simulation model, a more accurate assessment method, or a more sophisticated gamut mapping algorithm can yield a better result.

Spectral Reflectance Reconstruction of Organ Tissue Based on Metameric Black and Lattice Regression.

In this study, a new approach is proposed for the restoration of reflectance information on organ samples using a commercial camera. This novel approach is comprised of three stages. In the first stage, a color clustering method is utilized to extract the representative colors of the organ samples as well as their corresponding spectral reflectance. In the second stage, the spectral reflectance is decomposed into two separate parts, i.e., the fundamental stimulus spectrum and the metameric black following the matrix-R theory, and the latter is further utilized to form a look-up table (LUT) via a lattice regression model. Finally, the reflectance information can be easily retrieved by referring to the newly built LUT. The performance of the proposed method was investigated, along with that of six other commonly adopted methods, through a physical experiment using real, measured organ samples. The results demonstrate that the proposed method outperformed all the other methods in terms of both colorimetric and spectral metrics, indicating that it is a promising strategy for organ sample reflectance restoration.

Evaluation of the colour quality of display primary: Part I. Chromaticity based methods.

It is a challenge to determine the RGB primaries when designing a display. A big issue is to estimate the colorimetric performance of the display. In this paper, a systematic method was proposed to determine the best RGB primaries for a display. Nine testing metrics were implemented and they were divided into two groups (the gamut metrics and the colour related metrics). They were adopted to evaluate the performance of 52 displays having different RGB primary combinations. The results verified the proposed method. Some of the testing metrics gave similar results and it should be sufficient to choose some of them to reveal the overall performance of a display.

Color gamut mapping between small and large color gamuts: part II. gamut extension.

A wide color gamut (WCG) display has great color rendering capability and offers the opportunity to achieve a pleasing and realistic appearance in terms of image quality. To take full advantage of the large display gamut, a new gamut extension algorithm (GEA) is proposed based on a new color appearance scale, vividness. The performance of the new GEA was investigated via a psychophysical experiment together with five commonly used GEAs. In addition, two different uniform color spaces (UCSs) were also studied including the CAM02-UCS color space and a space, Jzazbz, designed for high dynamic range (HDR) and WCG applications. The results showed that the newly proposed GEA, i.e. the vividness-extension (VE) algorithm, outperformed all the other GEAs and the Jzazbz space was a promising UCS for evaluating gamut extension.

Colour gamut mapping between small and large colour gamuts: Part I. gamut compression.

This paper describes an investigation into the performance of different gamut compression algorithms (GCAs) in different uniform colour spaces (UCSs) between small and large colour gamuts. Gamut mapping is a key component in a colour management system and has drawn much attention in the last two decades. Two new GCAs, i.e. vividness-preserved (VP) and depth-preserved (DP), based on the concepts of 'vividness' and 'depth' are proposed and compared with the other commonly used GCAs with the exception of spatial GCAs since the goal of this study was to develop an algorithm that could be implemented in real time for mobile phone applications. In addition, UCSs including CIELAB, CAM02-UCS, and a newly developed UCS, Jzazbz, were tested to verify how they affect the performance of the GCAs. A psychophysical experiment was conducted and the results showed that one of the newly proposed GCAs, VP, gave the best performance among different GCAs and the Jzazbz is a promising UCS for gamut mapping.

Gloss evaluation from soft and hard metrologies.

Recent advances in bidirectional reflectance distribution function (BRDF) acquisitions have provided a novel approach for appearance measurement and analysis. In particular, since gloss appearance is dependent on the directional reflective properties of surfaces, it is reasonable to leverage the BRDF for gloss evaluation. In this paper, we investigate gloss appearance from both soft metrology and hard metrology. A psychophysical experiment was conducted for the gloss assessment of 47 neutral-color samples. In the evaluation of gloss perception from gloss meter measurements, we report several ambiguous correspondences in the medium gloss range. In order to analyze and explain this phenomenon, the BRDF was acquired and examined using a commercial BRDF measuring device. With an improved correlation-to-visual perception, we propose a two-dimensional gloss model by combining a parameter, the standard deviation of the specular lobe, from Ward's BRDF model with measured gloss values.

Measuring Perceptual Color Differences of Smartphone Photographs.

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this article, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.

Highly flexible and superhydrophobic MOF nanosheet membrane for ultrafast alcohol-water separation.

High-performance pervaporation membranes have potential in industrial separation applications, but overcoming the permeability-selectivity trade-off is a challenge. We report a strategy to create highly flexible metal-organic framework nanosheet (MOF-NS) membranes with a faveolate structure on polymer substrates for alcohol-water separation. The controlled growth followed by a surface-coating method effectively produced flexible and defect-free superhydrophobic MOF-NS membranes. The reversible deformation of the flexible MOF-NS and the vertical interlamellar pathways were captured with electron microscopy. Molecular simulations confirmed the structure and revealed transport mechanism. The ultrafast transport channels in MOF-NS exhibited an ultrahigh flux and a separation factor of 8.9 in the pervaporation of 5 weight % ethanol-water at 40°C, which can be used for biofuel recovery. MOF-NS and polydimethylsiloxane synergistically contribute to the separation performance.

Gamut mapping based image enhancement algorithm for color deficiencies.

This study presents an approach for enhancing color images for color vision deficiencies. The proposed approach is separated into three stages. First, the type and severity of a color deficient observer (CDO) were evaluated. Following that, the perceived color gamut was assessed using a physiologically-based color deficiency simulation model. Finally, images prepared for color normal observers (CNOs) were re-colored using a gamut mapping method to map colors from the gamut of a CNO to that of a CDO. Two psychophysical experiments were carried out to validate this method, and the results suggest that it is a promising solution for the CDOs. The unique feature of the present method is to include a gamut mapping method to enhance the color discrimination by preserving the perceived hue.

Cobalt encapsulated in bamboo-like N-doped carbon nanotubes for highly sensitive electroanalysis of Pb(II): enhancement based on adsorption and catalysis.

Carbon nanotubes (CNTs) are recognized as desirable candidates to fabricate electrochemical sensing interfaces owing to their high surface area and excellent electron conductivity. However, the poor catalytic properties of CNTs significantly hinder their further application in electrochemical detection. Herein, for the first time we combined defective CNTs with catalytically active cobalt nanoparticles (Co NPs) to give cobalt encapsulated in a bamboo-like N-doped carbon nanotube nanocomposite (Co/N-CNTs). The novel constructed Co/N-CNTs are used as a modifier on a bare glass carbon electrode for the electrochemical detection of Pb(ii). As a result, the positive effect of adsorption and catalysis on Co/N-CNT shows a significant improvement in the electroanalytical performance towards Pb(ii) with a sensitivity of 69.74 µA µM-1 and a limit of detection of 0.039 µM. Moreover, the stability and practical applications of Co/N-CNTs towards Pb(ii) in real water samples obtained from a mining subsidence area were also considered. This method shows great promise, achieving an outstanding electroanalysis efficiency with noble-metal-free nanocomposite sensors based on the combination of carbon and transition metals.