Color Research and Application最新文献

Color is a fundamental characteristic in textiles, particularly in interwoven mixed-color fabrics, where color appearance manifests as a nonlinear and complex phenomenon influenced by multiple factors, including yarn material, yarn color, weave structure, and the weaving process. Traditional mathematical models typically exhibit limitations in accurately capturing and managing this complexity. This study presents a deep learning approach utilizing convolutional neural networks (CNNs) to quantitatively analyze and predict the color characteristics of silk interwoven mixed-color fabrics. The primary objective of this research was to enhance the accuracy and stability of color predictions while enabling visual representations of the predicted outcomes. A comprehensive dataset of 1350 samples was established to train and validate the CNN model in color prediction tasks. Evaluation of the model's performance yielded a high predictive accuracy, with an average color difference (∆E ₀₀) of 0.96 between predicted and actual colors—a value close to the threshold of human visual indiscernibility. The results demonstrate that the CNN model offers precise and generalized color prediction capabilities, providing an intuitive visualization of predicted colors, with significant implications for textile design and production processes.

Chromatic adaptation to displays involves a complex interplay of factors, including surrounding light, background properties, and the luminance of stimuli. This study was aimed at understanding the impact of these factors on visual perception through a series of psychophysical experiments. Our findings show a significant interaction between background and surround conditions in adaptation responses. Two frameworks are proposed to capture the mixed adaptation mechanism. These models show promising results in predicting visual perception responses under various ambient environment conditions. This research offers insight into understanding chromatic adaptation in such complex conditions.

FAA-HF-STD-010A introduced a standard set of colors for coding air traffic control (ATC) displays. It was developed to accommodate viewers who have mild-to-moderate color-vision deficits (CVDs) without compromising usability for color normals. The test conditions were dark, which represents the majority of ATC environments, but that choice left the palette's suitability for use in brighter environments, such as ATC towers during daytime, unknown. We tested the palette under worst-case tower illumination and found that viewers with normal color vision and many with CVDs can recognize the colors almost perfectly if they can increase the display's luminance sufficiently to compensate—no Bezold–Brücke corrections are needed. Text rendered using all foreground/background combinations remains legible, too. The FAA standard color palette is usable under the full range of ambient illuminances encountered by US air traffic controllers and most other computer-display viewers. The colors are general-purpose and, therefore, suited for many purposes besides ATC that use color coding—especially if CVDs must be accommodated.

Fabrics from yarns of the same color but with different weaving parameters exhibit variations in their spectral properties, which are challenging to quantify description using physical models. This study proposes a bidirectional spectra prediction method for yarn and fabric based on a multi-output linear regression (MOLR) model based on the correlation analysis between the weaving parameters and spectra. Six yarns of different colors were used to weave fabric samples with varying textures by adjusting parameters such as weft density, reed count, and weave structure on an SGA598 fully automatic rapier loom. The spectral reflectance of both yarns and fabrics was measured using an X-Rite Color i7 spectrophotometer. Correlation analysis revealed a significant linear relationship between weft density and fabric spectral data. Therefore, a multi-output linear regression model was developed to predict the forward and backward spectral relationship between yarn and fabric based on weaving parameters. The models were evaluated using k-fold cross-validation and compared with random forest and multi-layer perceptron models. The results demonstrated that the forward spectra prediction model (yarn to fabric) achieved an average RMSE of 0.0055 and a coefficient of determination (R ²) of 0.9986, while the backward spectra prediction model (fabric to yarn) achieved an RMSE of 0.0065 and an R ² of 0.9947. These results indicate strong consistency between the predicted and measured spectra, with the proposed model outperforming random forest (RF) and multi-layer perceptron (MLP) models. This study provides methodological support for spectral measurement and analysis of customer fabric samples in the textile industry.

A new color gamut for real surface colors is proposed, based on 102 801 samples. This dataset encompasses a diverse range of artificial and natural materials. In order to define this gamut, a modified lattice regression interpolation algorithm was employed to define this gamut. The algorithm effectively balances accuracy and smoothness while maintaining convex hull properties, rendering the proposed gamut suitable for practical applications. A comparative analysis has been conducted between the proposed gamut and a variety of reference color gamuts, including those defined by ISO, Pointer, and the optimal colors. The results demonstrate that the proposed gamut is the most appropriate for the representation of real surface colors. Furthermore, to ascertain the impact of varying lighting conditions and different color spaces, the real surface color gamuts were derived under a range of correlated color temperatures (CCTs), spanning from 2000 to 10 000 K and across diverse color spaces. The findings indicate that the gamut volume initially increases with the increase of CCT, but then gradually and subtly declines when the CCT exceeds approximately 4000 K.

A new adaptive tone mapping algorithm, which includes global contrast compression, local detail enhancement, and dark region enhancement, is proposed in this paper for HDR images encoded with perceptual quantization (PQ) or hybrid log-gamma (HLG) transfer functions with the absolute luminance values. In particular, the global contrast compression adopts a luminance mapping curve considering the relationship between the HDR diffuse white and SDR nominal peak luminance, as well as the mapping of the skin tone. The local detail enhancement adopts an enhancement factor to enhance the details while avoiding halo effects. Moreover, the dark region enhancement is performed on nighttime images to enhance the visibility of details in dark regions. When compared to eight popular tone mapping methods, the proposed method is found to achieve a great global contrast, maintain local details, and also enhance great visibility of the details of nighttime images.