This study used computer software to design building models with selected colour parameters based on a two‐colour pattern combination, which was then presented on a standard computer screen. Further, ...the two‐colour combination pattern was adopted to prepare building simulation pictures with 153 colour combinations. A total of six hues were extracted based on the Natural Colour System, and there were also 18 colour samples with different lightness and saturation degrees. The results of the study found that hue difference, lightness difference, chroma difference, and total colour difference have a significant effect on harmony. On the other hand, hue difference, chroma difference, and total colour difference have a significant effect on preference. It is worth noting that lightness difference and architectural colour preferences were negatively related. In addition, there was a high correlation between architectural colour harmony and preference.
For the feature extraction of red–blue–green (RGB) colour images, researchers usually deal with R, G and B channels separately to obtain three feature vectors, and then combine them together to ...obtain a long real feature vector. This approach does not exploit the relationships between the three channels of the colour images. Recently, attention has been paid to quaternion features, which take the relationships between channels into consideration and seem to be more suitable for representing colour images. However, there are only a few quaternion classifiers for dealing with quaternion features. To meet this requirement, a new quaternion classifier, namely, the quaternion softmax classifier is proposed, which is an extended version of the conventional softmax classifier generally defined in the complex (or real) domain. The proposed quaternion softmax classifier is applied to two of the most common quaternion features, that is, the quaternion principal components analysis feature and the colour image pixel feature. The experimental results show that the proposed method performs better than the quaternion back propagation neural network in terms of accuracy and convergence rate.
The underwater environment is complex and ever-changing, and color distortion and reduced contrast are the two primary issues encountered in original underwater images. This article applies image ...enhancement to address these concerns by proposing a novel image color correction red channel correction after gray world (RAG) algorithm and an improved multiscale image fusion (IMF) algorithm for each problem. In terms of color correction, traditional algorithms typically implement a single color correction step, leading to issues such as locally unrealistic colors and limited applicability in certain scenes. The proposed RAG algorithm advances upon these methods by further dividing the input image into multiple local blocks after performing global color correction using the gray world algorithm. Secondary color corrections are then applied within each local block, overcoming the limitations of the traditional single-step color correction process. The corrected images achieve an underwater image colorfulness measure (UICM) mean value of 5.4671, representing an enhancement exceeding 20%. In contrast, the existing fusion algorithms often neglect the distinct characteristics of different channels and fail to maximize the advantages of fusion. The IMF algorithm proposed in this article calculates weights in the red green blue color space (RGB) and LAB color spaces of images to effectively incorporate channel disparities, refining the weights to provide a more thorough fusion process. The corrected images achieve a mean underwater color image quality evaluation (UCIQE) value of 0.630 and a mean colorfulness, contrast and fogdensity (CCF) value of 37.495. Significant improvements are observed across various other metrics.
In recent years, there has been a growing demand for the colorization of remote sensing images due to their inherent limitations caused by remote sensors, such as hazy or noisy atmospheric ...conditions. These factors result in the captured images needing to be clarified. Compared to ordinary images, remote sensing images present unique challenges in color recovery due to their imbalanced spatial distribution of objects. In this article, we propose a novel bidirectional layout-semantic-pixel joint decoupling and embedding network (BDEnet) following the idea of human painting to generate highly saturated color images with strong spatial consistency and object salience. The proposed BDEnet model emulates the process of human painting through a step-by-step approach. It begins by determining the overall tone of a large macroscopic region and progressively refining the local color based on this initial assessment. Specifically, BDEnet incorporates finer-grained semantics and pixel color information into a colored layout that represents a wide range of continuous areas, thereby accomplishing the colorization task. The BDEnet model operates at three scales, namely the layout (macro), semantic (medium), and pixel (micro) scales. It comprises three key modules: the multiscale feature decoupling (MFD) module, the layout-semantic-pixel multigranularity learning (MGL) module, and the semantic-pixel embedding (SPE) module. MFD module effectively reduces redundant noise from the semantic and layout scales by employing scale decoupling. This process ensures the extraction of efficient features essential for MGL. In the MGL module, three branches with different scales are employed to achieve layout division, semantic segmentation, and pixel coloring. To address the issue of insufficient category label guidance in layouts, we propose a novel approach called similar semantic merging (SSM) using a weakly supervised scheme to accomplish layout division. Finally, the SPE module incorporates stable semantic and pixel information into the layout features. This integration results in the generation of color images that exhibit strong spatial consistency, emphasize object salience, and possess high color saturation.
Underwater images often suffer from significant information loss in the red color channel, resulting in a predominantly bluish or greenish tone. Existing enhancement methods struggle to address this ...issue due to uniform enhancement applied to the bluish and greenish channels, resulting in overcompensation or under-compensation in the red channel. To address these challenges and achieve a more natural color restoration in underwater images, we propose the adaptive color compensation and enhancement (ACCE) algorithm. The ACCE algorithm comprises several essential steps. Initially, to recover the loss of red channel information more effectively, we divide the images into bluish and greenish components for preliminary color compensation (PCC) in the RGB color space. Subsequently, we introduce a novel minimum color loss (MCL) constraint to regulate the PCC, ensuring balanced histogram distributions across the RGB channels. Furthermore, for improved color balance in the enhanced underwater image, we design the fine-tuning color compensation (FCC) to the <inline-formula> <tex-math notation="LaTeX">a </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">b </tex-math></inline-formula> channels of the CIELAB color space. Ultimately, we employ the Contour Bougie (CB) enhancement algorithm to restore contour details in underwater images. Experimental results validate the superiority of the proposed ACCE algorithm over state-of-the-art methods, as demonstrated through qualitative and quantitative comparisons. In addition, ACCE exhibits promising generalization and potential for broader applications, encompassing tasks such as dehazing and lowlight image enhancement.
Coloration facilitates evolutionary investigations in nature because the interaction between genotype, phenotype and environment is relatively accessible. In a landmark set of studies, Endler ...addressed this complexity by demonstrating that the evolution of male Trinidadian guppy coloration is shaped by the local balance between selection for mate attractiveness versus crypsis. This became a textbook paradigm for how antagonistic selective pressures may determine evolutionary trajectories in nature. However, recent studies have challenged the generality of this paradigm. Here, we respond to these challenges by reviewing five important yet underappreciated factors that contribute to colour pattern evolution: (i) among-population variation in female preference and correlated variation in male coloration, (ii) differences in how predators versus conspecifics view males, (iii) biased assessment of pigmentary versus structural coloration, (iv) the importance of accounting for multi-species predator communities, and (v) the importance of considering the multivariate genetic architecture and multivariate context of selection and how sexual selection encourages polymorphic divergence. We elaborate these issues using two challenging papers. Our purpose is not to criticize but to point out the potential pitfalls in colour research and to emphasize the depth of consideration necessary for testing evolutionary hypotheses using complex multi-trait phenotypes such as guppy colour patterns.
To understand the function of colour signals in nature, we require robust quantitative analytical frameworks to enable us to estimate how animal and plant colour patterns appear against their natural ...background as viewed by ecologically relevant species. Due to the quantitative limitations of existing methods, colour and pattern are rarely analysed in conjunction with one another, despite a large body of literature and decades of research on the importance of spatio‐chromatic colour pattern analyses. Furthermore, key physiological limitations of animal visual systems such as spatial acuity, spectral sensitivities, photoreceptor abundances and receptor noise levels are rarely considered together in colour pattern analyses.
Here, we present a novel analytical framework, called the Quantitative Colour Pattern Analysis (QCPA). We have overcome many quantitative and qualitative limitations of existing colour pattern analyses by combining calibrated digital photography and visual modelling. We have integrated and updated existing spatio‐chromatic colour pattern analyses, including adjacency, visual contrast and boundary strength analysis, to be implemented using calibrated digital photography through the Multispectral Image Analysis and Calibration (MICA) Toolbox.
This combination of calibrated photography and spatio‐chromatic colour pattern analyses is enabled by the inclusion of psychophysical colour and luminance discrimination thresholds for image segmentation, which we call ‘Receptor Noise Limited Clustering’, used here for the first time. Furthermore, QCPA provides a novel psycho‐physiological approach to the modelling of spatial acuity using convolution in the spatial or frequency domains, followed by ‘Receptor Noise Limited Ranked Filtering’ to eliminate intermediate edge artefacts and recover sharp boundaries following smoothing. We also present a new type of colour pattern analysis, the ‘local edge intensity analysis’ as well as a range of novel psycho‐physiological approaches to the visualization of spatio‐chromatic data.
QCPA combines novel and existing pattern analysis frameworks into what we hope is a unified, free and open source toolbox and introduces a range of novel analytical and data‐visualization approaches. These analyses and tools have been seamlessly integrated into the MICA toolbox providing a dynamic and user‐friendly workflow.
The red spectrum is saliently attenuated due to the absorption and scattering properties of water. The acquired underwater images show severe color cast in underwater scenes. In this letter, we ...propose a novel color correction method for underwater images, which removes color cast on single pixels based on scene depth. The experimental results demonstrate that our approach can significantly improve the color effect and provide a correct input for the subsequent underwater image defogging methods.
Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data ...and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance (<inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>) in visible bands is 25.19%. We first measured <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> spectra of two classes of BOW BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2 and ordinary water in Shenyang. Then, in situ <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> data were converted into <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> corresponding to the wide GF-2 bands using the spectral response functions. We used the converted <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> data to calculate several band combinations, including the baseline height, <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>(green) <inline-formula> <tex-math notation="LaTeX">- R_{\mathrm {rs}} </tex-math></inline-formula>(red))/(<inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula>(green) <inline-formula> <tex-math notation="LaTeX">+ R_{\mathrm {rs}} </tex-math></inline-formula>(red), and the color purity on a Commission Internationale de L'Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, <inline-formula> <tex-math notation="LaTeX">R_{\mathrm {rs}} </tex-math></inline-formula> (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing.
In this study, an enhancement process obtained by applying the heat conduction equation of solid and stagnant fluids on colour images is proposed. After the colour channel stretching, the RGB colour ...image was converted to the HSI model. The heat conduction equation was applied for each pixel on the I channel of the HSI colour model. The elements of the feature matrix called heat conduction matrix (HCM) can have negative, positive or zero values. A pixel with a small negative HCM value indicates that I needs level enhancement for a good image, whereas a small positive HCM value means that the I level value will be reduced and aligned with its neighbours. High positive or negative values are defined as the edges of the objects and the I level values of such pixels are not changed to protect the edges. In addition, whether HCM is negative or positive, the balanced increment and decrement path at a level I ensures that the mean brightness value performs natural protection. Finally, an enhanced image is obtained by transitioning from the HSI to the RGB colour model. Experimental results show that this method can enhance colour image details better than other methods.
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