Distinct Color Research Articles

JADES + JEMS: A Detailed Look at the Buildup of Central Stellar Cores and Suppression of Star Formation in Galaxies at Redshifts 3 < z < 4.5

We present a spatially resolved study of stellar populations in six galaxies with stellar masses M * ∼ 1010 M ☉ at z ∼ 3.7 using 14-filter James Webb Space Telescope (JWST)/NIRCam imaging from the JADES and JEMS surveys. The six galaxies are visually selected to have clumpy substructures with distinct colors over rest frame 3600−4100 Å, including a red, dominant stellar core that is close to their stellar-light centroids. With 23-filter photometry from the Hubble Space Telescope to JWST, we measure the stellar-population properties of individual structural components via spectral energy distribution fitting using Prospector. We find that the central stellar cores are ≳2 times more massive than the Toomre mass, indicating they may not form via single in situ fragmentation. The stellar cores have stellar ages of 0.4−0.7 Gyr that are similar to the timescale of clump inward migration due to dynamical friction, suggesting that they likely instead formed through the coalescence of giant stellar clumps. While they have not yet quenched, the six galaxies are below the star-forming main sequence by 0.2−0.7 dex. Within each galaxy, we find that the specific star formation rate is lower in the central stellar core, and the stellar-mass surface density of the core is already similar to quenched galaxies of the same masses and redshifts. Meanwhile, the stellar ages of the cores are either comparable to or younger than the extended, smooth parts of the galaxies. Our findings are consistent with model predictions of the gas-rich compaction scenario for the buildup of galaxies’ central regions at high redshifts. We are likely witnessing the coeval formation of dense central cores, along with the onset of galaxy-wide quenching at z > 3.

Physically stressed bees expect less reward in an active choice judgement bias test.

Emotion-like states in animals are commonly assessed using judgment bias tests that measure judgements of ambiguous cues. Some studies have used these tests to argue for emotion-like states in insects. However, most of these results could have other explanations, including changes in motivation and attention. To control for these explanations, we developed a novel judgment bias test, requiring bumblebees to make an active choice indicating their interpretation of ambiguous stimuli. Bumblebees were trained to associate high or low rewards, in two different reward chambers, with distinct colours. We subsequently presented bees with ambiguous colours between the two learnt colours. In response, physically stressed bees were less likely than control bees to enter the reward chamber associated with high reward. Signal detection and drift diffusion models showed that stressed bees were more likely to choose low reward locations in response to ambiguous cues. The signal detection model further showed that the behaviour of stressed bees was explained by a reduction in the estimated probability of high rewards. We thus provide strong evidence for judgement biases in bees and suggest that their stress-induced behaviour is explained by reduced expectation of higher rewards, as expected for a pessimistic judgement bias.

A smartphone-assisted chromogenic and fluorogenic dual-channel sensor for specific determination of toxic Pb2+ ions with multifaceted applications

Heavy metal pollutants, such as Pb2+ ions, pose a substantial hazard to human health and ecosystems, and it was of great significance to develop cost-effective and portable techniques for online and visual detection of Pb2+. Herein, we fabricated a novel chromogenic and fluorogenic dual-channel sensor NQA through a Schiff base reaction involving a dicyanoisophorone derivative and isoniazid, enabling effective detection of Pb2+ ions. Upon complexation with Pb2+ ions at a 1:1 M ratio, the fluorescence intensity of NQA exhibited a remarkable enhancement. The naked-eye colorimetric detection utilizing NQA demonstrated a distinct color change to specific Pb2+ ions concentration from yellow to light pink. Based on this principle, a smartphone-assisted sensing platform was developed for visual and high-quality identification of Pb2+ in real samples. The UV binding constant for Pb2+ ions was determined to be 1.84 × 105 M−1, with a detection limit of 0.38 μM. Furthermore, the versatility of NQA extended to applications in naked-eye test paper experiments, the detection of Pb2+ ions in real water samples and living cells, showcasing its potential for environmental and biological monitoring.

Polarization-switchable structural colors generated by AlN metasurfaces and the effect of different substrates

Metasurfaces have the capability to boost the generation of distinct colors by improving the interaction between surface materials and photons. We present a straightforward and polarization-tunable aluminum nitride metasurface. This metasurface enables the display and concealment of the composition pattern and exhibits color switching by modifying the polarization state of the incident light. We further explore the impact of different substrates on metasurface performance. The results indicate that the full width at half maximum of the reflectance curves increases with the increase of the refractive index between the nanostructures and the substrate material, which leads to a broader dispersion of the structural colors in the spectrum. Moreover, the sizes of the nanostructures can be gradually reduced for the substrate with a high refractive index. These findings not only offer polarization-tunable structural color metasurfaces but also provide essential insights in selecting nanostructure and substrate materials, which will help in the design of nanostructures for such metasurfaces.

Agricultural label for egg production based on emergy accounting

This study aimed to present an innovative methodological proposal that integrates the ecological label tool with the Emergy accounting analysis methodology, with the purpose of developing an agricultural label for egg production grounded on Emergy accounting principles. The deliberate choice of this combination stems from the widespread recognition and extensive use of the ecological label tool for communicating sustainability information to society. However, it is important to note that some ecological labels have limitations, providing restricted information and presenting complexities that hinder their understanding. In this context, Emergy accounting was selected due to its capacity to convey a wide range of information in a direct and concise manner, condensing complex scenarios into easily understandable indexes. To achieve the proposed objective, several steps were undertaken. Initially, a comprehensive survey of scientific articles related to ecological labels was conducted, followed by exploration of existing ecological labels. Subsequently, a review of scientific literature was carried out to demonstrate the potential of Emergy accounting in comparing sustainability levels across different production systems. Then, the necessary steps of Emergy accounting were applied to evaluate four distinct egg production systems. These steps included defining, locating, and collecting data from the systems, creating Emergy diagrams, constructing Emergy tables, and evaluating the results. Additionally, this study established connections between the selected Emergy indexes and the ecological label Traffic Light Index. The culmination of this research was the methodological proposition of an agricultural label that integrates the Traffic Light Index eco-label. This label employs three distinct colors to signify differences in the sustainability of systems, utilizing Emergy accounting indexes both directly and indirectly to assess and compare them. The findings obtained offer valuable insights into how the fusion of Emergy accounting with the ecological label tool can serve as an effective management and communication tool. This methodological proposition holds significant potential for enhancing understanding and facilitating decisions related to sustainable practices and consumption.

Plant Species Classification and Biodiversity Estimation from UAV Images with Deep Learning

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F1 Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F1 Score. The relatively low F1 Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F1 Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use.

Smartphone-enabled composite of cellulose nanocrystals for rapid and selective mercury detection in cosmetics and water: insights from density functional theory studies

ABSTRACT The pervasive contamination of cosmetics and water with mercury ions (Hg(II)) poses severe health risks, driving the need for efficient methods for in situ detection and removal of it. Our study showcases a groundbreaking composite material that combines cellulose nanocrystals (CNCs) and smartphones for the purpose of rapid and selective mercury detection. The CNCs exhibit a distinctive rod-like morphology, presenting a high surface area that facilitates the accommodation of the Hg(II) ion probe, 4-(dimethylamino)phenyl)methanethione (DPM). This interaction leads to a distinct colour change from yellow to green, visible to the naked eye, enabling straightforward visual detection. Our composite is highly sensitive, with a limit of detection (LOD) of 1.76 × 10 − 8 M using digital image analysis and 3.6 × 10 − 8 M using spectrophotometric methods. These values are significantly below the safe drinking water guidelines set by the World Health Organization (WHO). Density functional theory (DFT) calculations were used to understand how Hg(II) ions interact with the DPM-CNC composite. These calculations confirmed that the material has a strong affinity for Hg(II) ions and demonstrated its selective detection capabilities. The DPM-CNC composite’s low detection threshold, rapid response time, and ease of regeneration make it a potent candidate for practical applications in mercury monitoring. This composite is a robust and reusable solution for real-time visualisation and quantification of mercury ions. It only requires 10 mg of material for effective measurements. It enhances safety in cosmetic products and water.

EXPLOITING ECO-FRIENDLY NATURAL DYES FROM PLANT SOURCES: EXTRACTION AND DIVERSE APPLICATIONS

Natural pigments extracted from plant sources offer eco-friendly and sustainable alternatives to synthetic colorants. In this study, pigment samples were obtained through aqueous extraction from beetroot (S1), red cabbage (S2), and turmeric (S3). Their physicochemical properties and potential applications were thoroughly evaluated. The research problem addressed in this study is the need to overcome the limitations of natural dyes, such a slower color fastness, reduced reproducibility, and limited color range, in order to promote their wider adoption in various industries as sustainable alternatives to synthetic colorants. The pigment extracts were assessed for their suitability in textile dyeing, paper pH indicators, food coloring, ink production, and natural stamp pad formulations. Cotton and wool fabrics were dyed by adding traditional mordants, and the pigment-treated textiles exhibited distinctive color changes and varying levels of color fastness. The pigment samples demonstrated the ability to act as effective acid-base indicators, displaying characteristic color transitions in response to varying pH levels. When used as natural food colorants, the extracts imparted attractive hues to rice and cream without altering their flavor profiles. The feasibility of incorporating these plant-derived pigments in ink manufacturing and stamp pad production was also explored. Beetroot pigment exhibited superior color retention and fastness properties, while red cabbage and turmeric extracts displayed effectiveness in selected applications. The findings of this comprehensive study highlight the viability of these plant sources as natural substitutes for synthetic colorants in diverse fields. The observed performance characteristics and the eco-friendly nature of the pigments underscore their potential to serve as sustainable alternatives in various industries.

Exploring the categorical nature of colour perception: Insights from artificial networks

The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vividly in several distinct colour categories. The origins and underlying mechanisms of this phenomenon remain partly unexplained. We investigate categorical colour perception in artificial neural networks (ANNs) using the odd-one-out paradigm. In the first experiment, we compared unimodal vision networks (e.g., ImageNet object recognition) to multimodal vision-language models (e.g., CLIP text-image matching). Our results show that vision networks predict a significant portion of human data (approximately 80%), while vision-language models account for the remaining unexplained data, even in non-linguistic experiments. These findings suggest that categorical colour perception is a language-independent representation, though it is partly shaped by linguistic colour terms during its development. In the second experiment, we explored how the visual task influences the colour categories of an ANN by examining twenty-four Taskonomy networks. Our results indicate that human-like colour categories are task-dependent, predominantly emerging in semantic and 3D tasks, with a notable absence in low-level tasks. To explain this difference, we analysed kernel responses before the winner-takes-all stage, observing that networks with mismatching colour categories may still align in underlying continuous representations. Our findings quantify the dual influence of visual signals and linguistic factors in categorical colour perception and demonstrate the task-dependent nature of this phenomenon, suggesting that categorical colour perception emerges to facilitate certain visual tasks.

Synthesis, Characterization and Analytical Study of New Azo Compounds for using as acid-base indictors

A diazotization method was used to synthesize new azo compounds, to which phenol was subsequently added to diazonium salt solution in basic medium at temperatures ranging from 0 to 5 °C and compounds were characterized by FT-IR, 1H-NMR, and 13C-NMR spectroscopy. The potential of the compounds as acid-base indicators was tested by determining their ionization and protonation constants at different pH levels (1 to 14) using a DMSO solution of the synthesized compounds. UV-Vis spectra shown as maximum wavelength (380-390 nm) observed at pH 1-7 occurs, indicating the cationic formula (protonated form of compound H2 and H4), another peak (470-530 nm) appears at pH 8-13, indicating the ionic formula (deprotonated form of compound H2 and H4), where for compound H5 observed at 560 nm at pH 1-7. Another peak appears 505 nm at pH 8-13 attributable to absorption the ionic formula (deprotonated form of compounds H5). Molecules H2, H4, and H5 showed distinct color changes when shifted to acidic and basic conditions. Furthermore, these compounds showed exceptional accuracy in drawing conclusions about the phenomenon.

Theoretically and Practically Efficient Maximum Defective Clique Search

The study of k -defective cliques, defined as induced subgraphs that differ from cliques by at most k missing edges, has attracted much attention in graph analysis due to their relevance in various applications, including social network analysis and implicit interaction predictions. However, determining the maximum k -defective clique in graphs has been proven to be an NP-hard problem, presenting significant challenges in finding an efficient solution. To address this problem, we develop a theoretically and practically efficient algorithm that leverages newly-designed branch reduction rules and a pivot-based branching technique. Our analysis establishes that the time complexity of the proposed algorithm is bounded by O(mγ k n ), where γ k is a real value strictly less than 2 (e.g., when k= 1, 2, and 3, γ k = 1.466, 1.755, and 1.889, respectively). To our knowledge, this algorithm achieves the best worst-case time complexity to date compared to state-of-the-art solutions. Moreover, to further reduce unnecessary branches, we propose a time-efficient upper bound-based pruning technique, which is obtained by manipulating information such as the number of distinct colors assigned to vertices and the presence of non-neighbors among them. Additionally, we employ an ordering-based heuristic approach as a preprocessing step to improve computational efficiency. Finally, we conduct extensive experiments on a diverse set of over 300 graphs to evaluate the efficiency of the proposed solutions. The results demonstrate that our algorithm achieves a speedup of 3 orders of magnitude over state-of-the-art solutions in processing most of real-world graphs.

Structurally-colored adhesives for sensitive, high-resolution, and non-invasive adhesion self-monitoring

Polymeric adhesives are critical in many applications, from daily life to implantable devices and soft robotics. Monitoring adhesion in a real-time and convenient manner before premature failure is essential yet challenging. Herein, we present structurally-colored adhesives for sensitive, high-resolution, and non-invasive adhesion self-monitoring via distinct color change for detecting subtle deformation and debonding. The structurally-colored adhesives are designed by integrating one-dimensional photonic nanochains into exemplified acrylate-copolymer-based adhesives and demonstrate distinct color-changing capability through a unique tilting mechanism of nanochains under shear. Our structurally-colored adhesives can be customized as pressure-sensitive and structural adhesives, exhibiting ultrafast response (<60 ms), high sensitivity, and high resolution (~120 μm). Moreover, predicting adhesion states and premature failure can be achieved assisted by imaging systems and machine learning algorithms with an average accuracy of up to 97.2%. Our structurally-colored adhesives are expected to offer a practical paradigm for structural health monitoring in the Internet of Things era.

Wafer-level flexible carbon-based film for fluorescent display and optical information storage

Fluorescent materials have garnered significant interest in optical display and information storage fields for their vibrant and distinct color presentation. Extensive research has been conducted on carbon-based fluorescent materials owing to their environmental sustainability, versatility, and adjustable optical properties. However, it is difficult to integrate high-resolution fluorescent structure on various wafer-level substrate for abundant optical display and information storage. This study successfully fabricated wafer-level carbon-based fluorescent flexible film with ∼100 μm thick using a standardized “lithography + annealing” process. The sample demonstrates high fluorescence intensity with a peak at 603 nm and fluorescence lifetime of 0.3517 ns. When excited by light of 365 nm, 485 nm, and 525 nm wavelengths, the film displays yellow, green, and red, showcasing exceptional color tuning capability. This flexible film is portable and can be readily integrated with various substrates, offering significant technical and theoretical support for advancing carbon-based optical display and information storage technologies.

A dual-response fluorescent hemicyanine probe for the detection of mitochondrial hypochlorite and viscosity based on ESIPT/AIE and TICT

Viscosity and hypochlorite (ClO−) are two important factors affecting the physiological activity and function of mitochondria. Hypochlorite (ClO−) is a vital reactive oxygen species, and endogenous ClO− acts as an important germicidal oxidant in the immune system at low concentrations. While, fluctuations in viscosity can cause mitochondrial dysfunction that is linked to several diseases. We herein report on a hemicyanine-based probe (BDTB-PA) for the detection of hypochlorite (ClO−) and viscosity. The probe exhibits a very low fluorescence background in an aqueous solution due to twisted intramolecular charge transfer (TICT) and blocked excited-state intramolecular proton transfer (ESIPT). In a viscous solution, the TICT of the probe was inhibited, generating a strong emission at 580 nm. In contrast, upon treatment with ClO−, the phenylboronic acid pinacol ester group of probe BDTB-PA was removed, and the hydroxyl group was released, activating the ESIPT process. Coupled with its aggregation-induced emission (AIE) characteristics, the product of BDTB-PA with ClO− exhibited a distinct emission at 540 nm. As such a solution of the probe BDTB-PA exhibited a distinct fluorescent color change from colourless to green under UV light with the addition of ClO−. More importantly, probe BDTB-PA was used for monitoring ClO− and viscosity in the mitochondria of living cells, plants and zebrafish.

Programmable Persistent Room Temperature Phosphorescence Switches through Wavelength-Selective Photoactivation.

Controlling multicolor persistent room-temperature phosphorescence (RTP) through photoirradiation holds fundamental significance but remains a significant challenge. In this study, we engineered a wavelength-selective photoresponsive system utilizing the Förster resonance energy transfer strategy. This system integrates a photoactivated long-lived luminescent material as the energy donor with a fluorescent photoswitch as the energy acceptor, facilitating programmable persistent luminescence switches. Distinct afterglow color states, such as initial nonemissive, green, yellow, and orange, were achieved through irradiation at 400 nm, 365 nm, and 254 nm, respectively. Based on this capability, we established an interacting network for multistate afterglow color switching among these four emissive states. In addition, we demonstrate the potential of this wavelength-selective photoresponsive system in the photo-controlled rewritable printing of multicolor afterglow images on a single thin film. This work represents a substantial step towards the fabrication of sophisticated wavelength-selective photoresponsive systems, potentially revolutionizing applications in optical data storage, security labeling, and smart displays by enabling precise control over photoresponsive behaviors under various photoirradiation wavelengths.

PTC-NH2@MOFs-AuNCs: A novel bifunctional signal probe for ratiometric electrochemiluminescence/colorimetry dual-mode aptasensing of lead ion

The development of a dual-mode sensing method for lead ions (Pb2+) is crucial and desirable. Herein, a single component-based bifunctional signal probe (perylene derivative-modified MOFs-gold nanoclusters, PTC-NH2@MOFs-AuNCs), which was capable of simultaneously generating an electrochemiluminescence (ECL) signal and exhibiting nanoenzyme activity, was prepared to fabricate a novel ratiometric ECL/colorimetry dual-mode platform for the visual, reliable, and sensitive analysis of Pb2+. Specifically, the PTC-NH2@MOFs-AuNCs were firstly synthesized via the electrostatic adsorption of AuNCs on the surface of PTC-NH2@MOFs. Upon the addition of Pb2+, the ECL signal of PTC-NH2@MOFs-AuNCs, which had been quenched by the adsorbed aptamer, could be restored at both the anode and cathode. Simultaneously, the peroxidase-like activity of PTC-NH2@MOFs-AuNCs was also recovered, accompanied by a distinct color transition from light blue to dark blue. The linear ranges of ratiometric ECL/colorimetric dual-modal assay for Pb2+ were 1 pM ∼ 5 nM and 1 nM ∼ 2 μM, with the limits of detection of 0.1 pM and 0.31 nM, respectively. More importantly, by introducing the screen-printed electrode and paper chip as sensing platform, a portable sensor for the convenient detection of Pb2+ was further established, demonstrating its promising practicability of rapid visual screening and accurate quantitative analysis in farmland soils.

A new species of Neblinaphryne (Anura: Brachycephaloidea: Neblinaphrynidae) from Serra do Imeri, Amazonas state, Brazil

The highlands of the Guiana Shield (Pantepui) in northern South America harbor a unique fauna and flora. However, this diversity remains poorly documented, as many Pantepui massifs remain little explored or unexplored, mainly because their access is very challenging. Considering amphibians, 11 genera are endemic or sub-endemic to Pantepui, and one of them, Neblinaphryne, is monospecific and was recently described from the Neblina massif, at the border between Brazil and Venezuela. We recently undertook an expedition in the nearby, previously uninventoried Imeri massif and discovered a new species of this genus. We describe this new species herein as Neblinaphryne imeri sp. nov., combining molecular, external morphological, acoustic, osteological and myological data. The new species shares with the other Neblinaphryne species (N. mayeri) minuscule septomaxillae and pointed terminal phalanges, confirming the morphological diagnostic characters of the genus. Nevertheless, the new species can promptly be distinguished from N. mayeri by having the head wider than long, a distinct color pattern, and prominent tubercles on the eyelid and humeral region, as well as osteological and genetic differences. These two species are likely endemic to their respective massifs, providing a striking new example of speciation by isolation within Pantepui, which was possibly mediated by climate and elevation, as previously hypothesized for many other lineages endemic to this region.

Preparation of chitosan/polyvinyl alcohol antibacterial indicator composite film loaded with AgNPs and purple sweet potato anthocyanins and its application in strawberry preservation

This study incorporated purple sweet potato anthocyanin (PSPA) and silver-nanoparticles (AgNPs) into the chitosan/polyvinyl alcohol film matrix (PVA/CS) to successfully prepare a composite film, which effectively inhibited bacterial growth and indicated product freshness. The addition of AgNPs and PSPA led to a dense structure of the film, which effectively enhanced its physical properties, barrier properties and functional properties. The incorporation of PSPA made the composite film highly pH-sensitive, which exhibited distinct color changes in varying pH solutions. The PVA/CS-AgNPs-PSPA10 composite film with PSPA and AgNPs resulted the shelf life of strawberries to 13 days at 4 °C, which effectively reduced strawberry breathing during storage. Additionally, such composite film changed color from purple to yellow-purple, indicating the deterioration of strawberries. It also showed an antibacterial indication through its excellent antibacterial property and freshness indication performance, which demonstrated its significance in developing antibacterial indicator composite packaging materials for fruits and vegetables preservation.

The Gall Wasp Ophelimus migdanorum and Its Parasitoid Closterocerus chamaeleon on Eucalyptus globulus at Two Sites with Different Rainfall Parameters in Bogotá, Colombia.

As a pest, the gall wasp Ophelimus migdanorum poses a risk to several Eucalyptus species in Colombia. In the tropical Andes, its biological development and the damage it causes can be influenced by climate, particularly rainfall. In this regard, we examined gall phenology, population fluctuation, and leaf damage caused by O. migdanorum, and its parasitoid Closterocerus chamaeleon, over 5months at two sites with contrasting rainfall in peri-urban areas of Bogotá, Colombia. Gall phenology and foliar damage were assessed on 10 trees per site. We characterized gall phenology by assessing their size and color, wasps' developmental stages, as well as affected leaf area and gall density on the leaf blade. Additionally, the individuals found in five attraction traps at each site were quantified biweekly to record population fluctuations. The effect of rainfall on wasp frequency and gall density between sampling sites and dates was compared using the chi-square test, while the relationship with rainfall was evaluated using the Kruskal-Wallis test. Only females of the gall wasp and its parasitoid were observed during the study, displaying multivoltine behavior. Six developmental stages of the galls were differentiated, each characterized by distinct coloration. Drier microclimates favored gall size, wasps' development stage frequency, population trend, and foliar damage. The general trend indicated a greater abundance of the parasitoid C. chamaeleon compared to O. migdanorum. Our study suggests that O. migdanorum has a less damaging effect on E. globulus in areas with rainy microclimates.

A Generative Adversarial Network for Upsampling of Direct Volume Rendering Images

AbstractDirect volume rendering (DVR) is an important tool for scientific and medical imaging visualization. Modern GPU acceleration has made DVR more accessible; however, the production of high‐quality rendered images with high frame rates is computationally expensive. We propose a deep learning method with a reduced computational demand. We leveraged a conditional generative adversarial network (cGAN) to upsample DVR images (a rendered scene), with a reduced sampling rate to obtain similar visual quality to that of a fully sampled method. Our dvrGAN is combined with a colour‐based loss function that is optimized for DVR images where different structures such as skin, bone, etc. are distinguished by assigning them distinct colours. The loss function highlights the structural differences between images, by examining pixel‐level colour, and thus helps identify, for instance, small bones in the limbs that may not be evident with reduced sampling rates. We evaluated our method in DVR of human computed tomography (CT) and CT angiography (CTA) volumes. Our method retained image quality and reduced computation time when compared to fully sampled methods and outperformed existing state‐of‐the‐art upsampling methods.