In this field study, a series of psychophysical tests were conducted to investigate the impact of indoor lighting on students’ visual perception and cognitive performance. A typical classroom of Wuhan University (China) was fitted with tubular light-emitting diode (LED) sources and LED panel sources in two experiments, respectively. Under the two lighting environments, seventy-nine college students were invited to complete a group of visual tests, which included colour preference evaluations of fruit and vegetables and skin tone, perceptual judgement on the atmosphere of the lighting environment, a reading comfort assessment concerning different paper colours, a Karolinska Sleepiness Scale (KSS) test quantifying alertness, and the Anfimov test of attention (also concerning paper colours). These tests were conducted twice, before and after a two-hour self-study under each lighting environment, with the aim of investigating the impact of visual fatigue on visual perception and cognitive performance. It was found that the influence of indoor lighting was significant on skin preference and atmosphere perception, while no measured effects of lighting were observed on participant’s alertness and attention. Meanwhile, the impact of visual fatigue was also found to be insignificant in this case. Interestingly, paper colour, rather than indoor lighting, was found to have a significant impact on the visual comfort of text reading. In addition, degree of proficiency significantly influenced the proofreading speed and accuracy of the subjects the in Anfimov test, which we believe should be taken into consideration when implementing similar tests in follow-up studies. The preliminary findings of this field study should provide a deeper understanding of how changes in classroom lighting contribute to visual perception and cognitive performance of occupants.

This paper proposes an improved reflectance reconstruction method by adaptively selecting training samples. Modified Principal Component Analysis estimation was proposed by orthogonal regression considering the system noise; deriving the optimum number of training samples by BP-Adaboost neural network; and grouping the representative samples together by hierarchical cluster analysis from a large database of samples. Finally, the training samples were selected by colorimetric subspace tracking. Experimental results indicated that the proposed method significantly outperforms the traditional methods in terms of both spectral and colorimetric accuracy, and our reflectance modeling is a reasonable and convenient tool to generate adaptive training sets.

An improved spectral reflectance estimation method was developed to transform captured RGB images to spectral reflectance. The novelty of our method is an iteratively reweighted regulated model that combines polynomial expansion signals, which was developed for spectral reflectance estimation, and a cross-polarized imaging system, which is used to eliminate glare and specular highlights. Two RGB images are captured under two illumination conditions. The method was tested using ColorChecker charts. The results demonstrate that the proposed method could make a significant improvement of the accuracy in both spectral and colorimetric: it can achieve 23.8% improved accuracy in mean CIEDE2000 color difference, while it achieves 24.6% improved accuracy in RMS error compared with classic regularized least squares (RLS) method. The proposed method is sufficiently accurate in predicting the spectral properties and their performance within an acceptable range, i.e., typical customer tolerance of less than 3 DE units in the graphic arts industry.

Restoring the correct or realistic color of a cultural heritage object is a crucial problem for imaging techniques. Digital images often have undesired color casts due to adverse effects caused by unstable illuminant conditions, vignetting, and color changes due to camera settings. In this work, we present an improved color correction method for color cast images that makes the color appear more realistic. It is based on a computational model of the human visual system that perceives objects by color constancy theory; it realizes illumination non-uniformity compensation and chromaticity correction for color cast images by taking into account the color stability of some pigments. This approach has been used to correct the color in Cave 465 of the Mogao Grottoes. The experimental results demonstrate that the proposed method is able to “adaptively correct” color cast images with widely varying lighting conditions and improve the consistency efficaciously. It can achieve improved consistency in the mean CIEDE2000 color difference compared with the images before correction. This colorimetric correction methodology is sufficiently accurate in color correction implementation for cast images of murals captured in the early years.

Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wolf algorithm was scaled up to two dimensions and combined with an LSTM model for optimal parameter search. Second, six pigments commonly used in painted artifacts were subjected to simulated aging experiments. Third, by applying the experimental data to different predictors, the results of the Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Long Short-Term Memory on Gray Wolf Optimizer (GWO-LSTM), and GWOAD-LSTM models were compared. The results showed that our proposed GWOAD-LSTM model outperformed other models in terms of accuracy and generalization ability, especially in predicting hLC color attributes. Our study aimed to provide a new application tool for the restoration and rejuvenation of painted artifacts.

The pigments of cultural color paintings have faded with the passage of time. Color segmentations are essential for digital color reconstruction, but the complexity of color paintings makes it challenging to achieve high-precision segmentation using previous methods. To address the challenges of color painting segmentation, an optimized strategy based on U-Net is proposed in this paper. The residual blocks of a residual network (ResNet) are added to the original U-Net architecture, and a UR-Net is constructed for the semantic segmentation of color paintings. The following steps are taken. First, datasets of color paintings are obtained as training and test samples and are labeled with the two following pixel colors: earth red and ultramarine blue. Second, residual blocks are improved and added to fit the U-Net architecture. Then, a UR-Net is constructed and trained using the samples to obtain the semantic segmentation model. Finally, the effectiveness of the trained UR-Net model for segmenting the test samples is evaluated, and it is compared with the K-means clustering algorithm, ResNet, and U-Net. Data from several studies suggest that the segmentation accuracy of the UR-Net model is higher than that of other methods for the color segmentation of painted images, and the IoUs of the segmented earth red and ultramarine blue pixels are 0.9346 and 0.9259, respectively, achieving the desired results. The proposed UR-Net model provides theoretical and methodological support for further in-depth research on color recognition and segmentation of cultural color paintings.