Spectral missing color correction based on an adaptive parameter fitting model.

With the development of remote sensing technology, true-color visualization of hyperspectral LiDAR echo signals has become a hotspot for both academic research and commercial applications. The limitation of the emission power of hyperspectral LiDAR causes the loss of spectral-reflectance information in some channels of the hyperspectral LiDAR echo signal. The color reconstructed based on the hyperspectral LiDAR echo signal is bound to have serious color cast problem. To solve the existing problem, a spectral missing color correction approach based on adaptive parameter fitting model is proposed in this study. Given the known missing spectral-reflectance band intervals, the colors in incomplete spectral integration are corrected to accurately restore target colors. Based on the experimental results, the color difference between color blocks and the hyperspectral image corrected by the proposed color correction model is smaller than that of the ground truth, and the image quality is higher, realizing the accurate reproduction of the target color.

A potential endogenous gibberellin-mediated signaling cascade regulated floral transition in Magnolia × soulangeana 'Changchun'.

The floral transition is a critical developmental switch in plants, and has profound effects on the flower production and yield. Magnolia × soulangeana 'Changchun' is known as a woody ornamental plant, which can bloom in spring and summer, respectively. In this study, anatomical observation, physiological measurement, transcriptome, and small RNA sequencing were performed to investigate potential endogenous regulatory mechanisms underlying floral transition in 'Changchun'. Transition of the shoot apical meristem from vegetative to reproductive growth occurred between late April and early May. During this specific developmental process, a total of 161,645 unigenes were identified, of which 73,257 were significantly differentially expressed, while a number of these two categories of miRNAs were 299 and 148, respectively. Further analysis of differentially expressed genes (DEGs) revealed that gibberellin signaling could regulate floral transition in 'Changchun' in a DELLA-dependent manner. In addition, prediction and analysis of miRNA targeted genes suggested that another potential molecular regulatory module was mediated by the miR172 family and other several novel miRNAs (Ms-novel_miR139, Ms-novel_miR229, and Ms-novel_miR232), with the participation of up- or down-regulating genes, including MsSVP, MsAP2, MsTOE3, MsAP1, MsGATA6, MsE2FA, and MsMDS6. Through the integrated analysis of mRNA and miRNA, our research results will facilitate the understanding of the potential molecular mechanism underlying floral transition in 'Changchun', and also provide basic experimental data for the plant germplasm resources innovation in Magnolia.

Optimized light source spectral power distribution for RGB camera based spectral reflectance recovery.

The accuracy of recovered spectra from camera responses mainly depends on the spectral estimation algorithm used, the camera and filters selected, and the light source used to illuminate the object. We present and compare different light source spectrum optimization methods together with different spectral estimation algorithms applied to reflectance recovery. These optimization methods include the Monte Carlo (MC) method, particle swarm optimization (PSO) and multi-population genetic algorithm (MPGA). Optimized SPDs are compared with D65, D50 A and three LED light sources in simulation and reality. Results obtained show us that MPGA has superior performance, and optimized light source spectra along with better spectral estimation algorithm can provide a more accurate spectral reflectance estimation of an object surface. Meanwhile, it is found that camera spectral sensitivities weighted by optimized SPDs tend to be mutually orthogonal.

Sequential adaptive estimation for spectral reflectance based on camera responses.

A sequential weighted nonlinear regression technique from digital camera responses is proposed for spectral reflectance estimation. The method consists of two stages taking colorimetric and spectral errors between training set and target set into accounts successively. Based on polynomial expansion model, local optimal training samples are adaptively employed to recover spectral reflectance as accurately as possible. The performance of the method is compared with several existing methods in the cases of simulated camera responses under three kinds of noise levels and practical camera responses under the self as well as cross test conditions. Results show that the proposed method is able to recover spectral reflectance with a higher accuracy than other methods considered.

Reflectance spectra reconstruction from trichromatic camera based on kernel partial least square method.

A novel spectral reflectance reconstruction method based on kernel partial least square (KPLS) regression is proposed. The proposed method integrates the partial least square algorithm and kernel function to estimate the reflectance spectra from 9-channel multispectral imaging system using commercial trichromatic camera. The performance of the proposed method is demonstrated in comparison with the existing methods using simulated and real camera responses from Munsell Matte color and IT8.7/3 dataset. The experimental results show that the proposed method is superior or at least equivalent to its counterparts and satisfactory enough for color management purpose.

Spectra estimation from raw camera responses based on adaptive local-weighted linear regression.

An improved spectral reflectance estimation method is developed to transform raw camera RGB responses to spectral reflectance. The novelty of our method is to apply a local weighted linear regression model for spectral reflectance estimation and construct the weighting matrix using a Gaussian function in CIELAB uniform color space. The proposed method was tested using both a standard color chart and a set of textile samples, with a digital RGB camera and by ten times ten-fold cross-validation. The results demonstrate that our method gives the best accuracy in estimating both the spectral reflectance and the colorimetric values in comparison with existing methods.

Classification and influencing factors analysis of facial skin color in Chinese population.

BACKGROUND: Subjective evaluation and quantitative analysis of skin color are very important both in dermatology and in cosmetology, but results of existing objective classifications tend to be incompatible to visual perception of skin color among females and males in China. METHODS: Skin color at four facial skin sites was measured in 409 volunteers with 219 males and 190 females aged 20-79 years. Ten observers with normal color vision assessed facial skin color of 205 subjects. The relationship between visual scores and Chardon's individual typology angle (ITA°) values was investigated. Then, the variations of color in different sub-groups were comprehensively discussed by skin color data of 409 volunteers. RESULTS: In order to better characterize Chinese skin color, the redness parameter must be added into the ITA formula. By comparing skin color of Chinese living in different environments or with different lifestyles, some factors not conducive to brightening skin have been identified. CONCLUSION: This study indicated the different influence of skin color components on perceived brightness between Caucasian and Chinese. And skin color was significantly affected by related variables such as gender, age, working environment, and lifestyles. Therefore, this study suggests how to brighten skin color in daily life.

[Research on Visual Perception-Referenced Compression Method for Multi-Spectral Data with High-Fidelity].

Aim In order to maintain the chromaticity precision in the process of linear compression of the multispectral data, a visual perception-referenced compression method (VPCM) based on the chroma gradient (refer to the partial derivative of chroma to wavelength) is proposed. Method The method firstly successfully developed the transfer functions which could synchronously fusion the spectral features and chromaticity characteristics of human visuals based on the nonlinear analytic feature of human visual system. For further improvement the transfer function, a modified optimizing function was developed to help find out the optimal transfer direction for different sample sets. If the transfer function was finally settled, it will be applied to transforming the spectral data of the sample set (Γ(S)=C). Then the transformed spectral data of the sample set will be compressed with high chromatic accuracy by the principle components analysis method. After that, the compressed data will be reconstructed through inverse transformation (Γ(-1)(C)=), while the reconstructed spectral data will be using to evaluate the effective of the proposed VPCM method. Result Four groups typical and representative sample sets were chosen to test the effective of the proposed method. The CIELab color difference in the D50/2° calculates condition and a proposed mean metamerism index (MMI) calculated with 75 groups typical light sources (including tungsten, fluorescent and LED lamp) was adopted as evaluating metrics. Eventually, the comparative experiment involving several existing methods Lab-PQR and 2-XYZ indicates that the proposed VPCM hold the best chromatic accuracy both for metric MMI and the average color difference ΔE(ab) when compared with Lab-PQR and 2-XYZ, and the spectral accuracy was calculated between Lab-PQR and 2-XYZ with Lab-PQR maintained the highest spectral accuracy. Conclusion The proposed VPCM can preserve high compression chromatic precision at the price of small loss of spectral precision and possess good colorimetric stability under variable reference conditions. It is very applicable for some application fields which require compressing of the multi-spectral data with high chromatic accuracy.

Updated version of an interim connection space LabPQR for spectral color reproduction: LabLab.

In this paper, we propose a new interim connection space (ICS) called LabLab, which is an updated version of LabPQR, to overcome the drawback that the last three dimensions of LabPQR have no definite colorimetric meanings. We extended and improved the method by which the first three dimensions of LabPQR are deduced to obtain an ICS consisting of two sets of CIELAB values under different illuminants, and the reconstructed spectra from LabLab were obtained by minimizing colorimetric errors by means of the computational formula of the CIE-XYZ tristimulus values combined with least-squares best fit. The improvement obtained from the proposed method was tested to compress and reconstruct the reflectance spectra of the 1950 Natural Color System color chips and more than 50,000 ISO SOCS color patches as well as six multispectral images acquired by multispectral image acquisition systems using 1600 glossy Munsell color chips as training samples. The performance was evaluated by the mean values of color differences between the original and reconstructed spectra under the CIE 1931 standard colorimetric observer and the CIE standard illuminants D50, D55, D65, D75, F2, F7, F11, and A as well as five multichip white LED light sources. The mean and maximum values of the root mean square errors between the original and reconstructed spectra were also calculated. The experimental results show that the proposed three LabLab interim connection spaces significantly outperform principal component analysis, LabPQR, XYZLMS, Fairman-Brill, and LabRGB in colorimetric reconstruction accuracy at the cost of slight reduction of spectral reconstruction accuracy and illuminant independence of color differences of the suggested LabLab interim connection spaces outperform other interim connection spaces. In addition, the presented LabLab interim connection spaces could be quite compatible with the extensively used colorimetric management system since each dimension has definite colorimetric meanings and is perceptually uniform.

[Iterated Tikhonov Regularization for Spectral Recovery from Tristimulus].

Reflective spectra in a multispectral image can objectively and originally represent color information due to their high dimensionality, illuminant independent and device independent. Aiming to the problem of loss of spectral information when the spectral data reconstructed from three-dimensional colorimetric data in the trichromatic camera-based spectral image acquisition system and its subsequent problem of loss of color information, this work proposes an iterated Tikhonov regularization to reconstruct the reflectance spectra. First of all, according to relationship between the colorimetric value and the reflective spectra in the colorimetric theory, this work constructs a spectral reconstruction equation which can reconstruct high dimensional spectral data from three dimensional colorimetric data acquired by the trichromatic camera. Then, the iterated Tikhonov regularization, inspired by the idea of the pseudo inverse Moore-Penrose, is used to cope with the linear ill-posed inverse problem during solving the equation of reconstructing reflectance spectra. Meanwhile, the work also uses the L-curve method to obtain an optimal regularized parameter of the iterated Tikhonov regularization by training a set of samples. Through these methods, the ill condition of the spectral reconstruction equation can be effectively controlled and improved, and subsequently loss of spectral information of the reconstructed spectral data can be reduced. The verification experiment is performed under another set of training samples. The experimental results show that the proposed method reconstructs the reflective spectra with less spectral information loss in the trichromatic camera-based spectral image acquisition system, which reflects in obvious decreases of spectral errors and colorimetric errors compared with the previous method.

[Research on the Training Samples Selection for Spectral Reflectance Reconstruction Based on Principal Component Analysis].

The composition of training samples set is an important influence factor of spectral reflectance reconstruction process. Representative color samples selection for learning-based spectral reflectance reconstruction is discussed in this paper. A method based on Principal Component Analysis (PCA) is proposed to perform sample selection. First of all, a part of samples are selected according to the minimum Euclidean distance criteria in terms of camera response value from a large number of samples, which aim to ensure the similarity between training samples and target samples. Then the PCA data processing method is applied to these samples after removing the duplicate samples. The samples with larger principal component loadings are regarded as the representative color samples. Different thresholds for each principal component are used to make decision whether the loading of sample is large enough. In order to validate the proposed method, the selected samples are used as training samples to recover the spectral reflectance of color patches. A real multi-channel imaging system by loading broadband color filters in front of lens is used in the experiment to acquire the multi-channel image dataset. In this paper the pseudo-inverse method is employed to reconstruct spectral reflectance of target color patches. It is shown that the proposed method is superior to the previous methods in spectral reconstruction accuracy and can meet the requirements of high precision color reproduction.

[Research on Developping the Colorimetric Correction Method Based on Wide Gamut Spectral Color Dataset].

In cross-media digital color imaging workflow, the colorimetric information transform among various digital medias always suffers from large transform errors, due to the difference among the lighting illuminants and the digital devices. Colorimetric correction aims at avoiding the colorimetric transform mismatch and thus improving the color transform accuracy comprehensively. Till now, two kinds of colorimetric transform methods have been proposed, which are the chromatic adaptation transform and the regression-based transform. However, since the color gamut of the training samples of such two method are both small, adopting those methods in colorimetric domain always leads to large colorimetric transform errors for the high saturated color regions. In this research, in order to reduce the large correction error in high saturated color regions, a modified colorimetric correction method basing on a wide gamut spectral dataset was proposed. The wide gamut spectral dataset was built by comprehensively collecting and producing typical spectral color samples and could provide optimal training samples for the existing regression based colorimetric correction model, with the help of gamut partition and optimal color purity choosing. By modifying the existing method with such samples, the colorimetric correction performance obviously improved. The experimental result shows that the modified colorimetric correction method performs significantly better than the existing methods and the colorimetric correction errors are successfully reduced by around 15% according to proposed method in form of CIEDE2000 color difference, while as for the high saturated color regions the reduction rate of the colorimetric correction errors approximately grows to 40%. The authors believe that the proposed method will provided effective support for the development of digital color imaging in near future.

[Optimization Study of Non-Contact Color Measurement Parameters].

Spectral data or chrominance data acquired with color measurement device is able to objectively characterize the true color information of the object. As to the measuring target that without direct contact with the measuring instrument, it needs to adopt the non-contact color measurement method to obtain the color information.The spectral irradiance theoretical analysis shows that the changes of the color illumination distance, measuring distance and exposure time have greater impact on the measurement results. However, there is no in-depth study on the influence of the changes of measuring parameters for the measured results. In order to get the most accurate measurement results in the process of non-contact color measurement, parameter optimization study based on orthogonal experiment was proposed. Experiment with different combinations of parameters was conducted to obtain the color information of ColorChecker via PR705 spectroradiometer with range analysis and variance analysis method.Experimental results show that the minimum color difference of measured value and the standard value is 0.878 8ΔE, and the maximum color difference is 1.543 1ΔE. Data results show that the proposed method can effectively select the best combination of parameters, and to analyze the impact of various parameters on the result.

Research on the Development of Light Blending Model for Smart LED Lighting.

The color of the LED smart light is tunable by its inner equipped micro-processing systems. Therefore, it could provide significant improvement for the smart lighting conditions, such as museum lighting and home lighting. At present, the limitation of the current lighting blending technology remarkably affects the application of smart lighting technology and people could not make full use of the adjustability of the smart luminaries. In this research, a novel light blending model was proposed based on BP neural network and active set algorithm. The models could effectively simulate the nonlinear relationship between the device control values of the smart light and the output radiance spectrum of the light. Particularly, a BP neural network-based forward model for LED light blending was firstly proposed, which could accurately calculate the spectral radiance power distribution from the device control values. Afterwards, based on forward model, an active set algorithm-based backward model was developed, which could precisely predict the device control values from the desired spectral radiance power distribution. The experimental result indicates that the proposed method could accurately achieve the light blending controlling of smart LED light, with a CIEUCS Duv value of 0.002 7, which is significantly smaller than the just noticeable difference value of human vision. The authors believe that the proposed method will provided effective support for the development of smart LED lighting in near future.

Research on Assessment Methods of Spectral Reflectance Data Quality for Computer Color Matching.

Spectral reflectance data quality is important for computer color matching. There are two existing methods for evaluating the quality­spectral reflectance method and K/S method, which are too complex to apply. In this paper, 45°/0° and d/8° geometric conditions are used in the measurement of spectral reflectance of the offset ink samples printed on coated paper and silver-foiled paper while improvement on the geometric condition is made on the basis of the spectral reflectance method. Moreover, a new evaluation method­lightness and chromaticity comparative method is put forward, and comparison is made among the three methods. The results show that both 45°/0° and d/8° are feasible in the measurement of spectral reflectance of coated paper; however the former one cannot meet the requirement of spectral reflectance measurement of silver-foiled paper. In addition, as to d/8° Specular Component Included (SCI), when the silver-foiled paper is taken as the substrate, the reflectance of transparent white ink samples are smaller than that of other primary inks; and abnormal intersections appear in the curves of cyan and magenta ink respectively at the concentration of 60%, resulting in a poor spectra quality at high ink concentration; In the figure of lightness and chromaticity curves, there is significant divergence of the cyan and magenta ink curves from the referenced coated paper. In conclusion, the spectral reflectance of the transparent ink should be greater than or at least equal to other primary inks, and the maximum concentration of cyan and magenta should be limited; when the coated paper with good diffusion performance is taken as the reference, the comparative analysis is more intuitive than the two existing methods.

Optimization of spectral printer modeling based on a modified cellular Yule-Nielsen spectral Neugebauer model.

The study presented here optimizes several steps in the spectral printer modeling workflow based on a cellular Yule-Nielsen spectral Neugebauer (CYNSN) model. First, a printer subdividing method was developed that reduces the number of sub-models while maintaining the maximum device gamut. Second, the forward spectral prediction accuracy of the CYNSN model for each subspace of the printer was improved using back propagation artificial neural network (BPANN) estimated n values. Third, a sequential gamut judging method, which clearly reduced the complexity of the optimal sub-model and cell searching process during printer backward modeling, was proposed. After that, we further modified the use of the modeling color metric and comprehensively improved the spectral and perceptual accuracy of the spectral printer model. The experimental results show that the proposed optimization approaches provide obvious improvements in aspects of the modeling accuracy or efficiency for each of the corresponding steps, and an overall improvement of the optimized spectral printer modeling workflow was also demonstrated.

Carcinogenic Role and Clinical Significance of Histone H3-H4 Chaperone Anti-silencing Function 1 B (ASF1B) in Lung Adenocarcinoma.

Histone H3-H4 chaperone anti-silencing function 1 (ASF1) plays an important role in the polymerization, transport, and modification of histones. However, the significance of ASF1B in lung adenocarcinoma (LUAD) is largely overlooked. We investigated the aberrant expression of ASF1B in LUAD and its potential link to patient survival using multiple databases. ASF1B-overexpressing and knockdown cell lines were constructed to explore its effects on the biological behavior of lung cancer cells. ssGSEA, TMB, TIDE and IMvigor210 cohort were used to explore and validate the association of ASF1B to tumor immunity. Our data suggested that ASF1B was overexpressed in LUAD, and was associated with poor prognosis. ASF1B promoted the proliferation, migration, and invasion of lung cancer cells by regulating the phosphorylation of AKT in vitro. ASF1B was associated with tumor immunity. In summary, ASF1B may promote malignant behavior of LUAD cells, and its overexpression correlates with worse prognosis and better immunotherapy effect.

[The study on spectral reflectance reconstruction based on wideband multi-spectral acquisition system].

The multispectral image acquisition oriented to reproduction requests that the data is device independent and scenes independent, and can realize the characterization of the original color information. Aiming at disturbance, noise error of system, and the requirement for training samples' typical representative and correlation, the authors proposed orthogonal regression spectral algorithm and training samples selection algorithm based on subspace tracking, through the mapping function between the spectral space and color space, by selecting the best samples in typical representative and correlation samples between target samples and selected samples. The modified Sinar 75H trichromatic digital camera combined with bandpass filter glasses were used for experiment, the data show that our method has higher spectral and chromaticity accuracy, the training samples selected by subspace tracking method are uniformly distributed in the sample space, and have good orthogonality. The statistics experimental results indicate that the performance of the proposed method is obviously better than that of previous method, in both color difference error and spectral reflectance error.

[Research on developping the spectral dataset for Dunhuang typical colors based on color constancy].

The present paper aims at developping a method to reasonably set up the typical spectral color dataset for different kinds of Chinese cultural heritage in color rendering process. The world famous wall paintings dating from more than 1700 years ago in Dunhuang Mogao Grottoes was taken as typical case in this research. In order to maintain the color constancy during the color rendering workflow of Dunhuang culture relics, a chromatic adaptation based method for developping the spectral dataset of typical colors for those wall paintings was proposed from the view point of human vision perception ability. Under the help and guidance of researchers in the art-research institution and protection-research institution of Dunhuang Academy and according to the existing research achievement of Dunhuang Research in the past years, 48 typical known Dunhuang pigments were chosen and 240 representative color samples were made with reflective spectral ranging from 360 to 750 nm was acquired by a spectrometer. In order to find the typical colors of the above mentioned color samples, the original dataset was devided into several subgroups by clustering analysis. The grouping number, together with the most typical samples for each subgroup which made up the firstly built typical color dataset, was determined by wilcoxon signed rank test according to the color inconstancy index comprehensively calculated under 6 typical illuminating conditions. Considering the completeness of gamut of Dunhuang wall paintings, 8 complementary colors was determined and finally the typical spectral color dataset was built up which contains 100 representative spectral colors. The analytical calculating results show that the median color inconstancy index of the built dataset in 99% confidence level by wilcoxon signed rank test was 3.28 and the 100 colors are distributing in the whole gamut uniformly, which ensures that this dataset can provide reasonable reference for choosing the color with highest color constancy during the color rendering process of Dunhuang cultural heritage.

An improved spectral estimation method based on color perception features of mobile phone camera.

We use the mobile phone camera as a new spectral imaging device to obtain raw responses of samples for spectral estimation and propose an improved sequential adaptive weighted spectral estimation method. First, we verify the linearity of the raw response of the cell phone camera and investigate its feasibility for spectral estimation experiments. Then, we propose a sequential adaptive spectral estimation method based on the CIE1976 L*a*b* (CIELAB) uniform color space color perception feature. The first stage of the method is to weight the training samples and perform the first spectral reflectance estimation by considering the Lab color space color perception features differences between samples, and the second stage is to adaptively select the locally optimal training samples and weight them by the first estimated root mean square error (RMSE), and perform the second spectral reconstruction. The novelty of the method is to weight the samples by using the sample in CIELAB uniform color space perception features to more accurately characterize the color difference. By comparing with several existing methods, the results show that the method has the best performance in both spectral error and chromaticity error. Finally, we apply this weighting strategy based on the CIELAB color space color perception feature to the existing method, and the spectral estimation performance is greatly improved compared with that before the application, which proves the effectiveness of this weighting method.