Color Adjustment Research Articles

A two-stage HDR reconstruction pipeline for extreme dark-light RGGB images

RGGB sensor arrays are commonly used in digital cameras and mobile photography. However, images of extreme dark-light conditions often suffer from insufficient exposure because the sensor receives insufficient light. The existing methods mainly employ U-Net variants, multi-stage camera parameter simulation, or image parameter processing to address this issue. However, those methods usually apply color adjustments evenly across the entire image, which may cause extensive blue or green noise artifacts, especially in images with dark backgrounds. This study attacks the problem by proposing a novel multi-step process for image enhancement. The pipeline starts with a self-attention U-Net for initial color restoration and applies a Color Correction Matrix (CCM). Thereafter, High Dynamic Range (HDR) image reconstruction techniques are utilized to improve exposure using various Camera Response Functions (CRFs). After removing under- and over-exposed frames, pseudo-HDR images are created through multi-frame fusion. Also, a comparative analysis is conducted based on a standard dataset, and the results show that the proposed approach performs better in creating well-exposed images and improves the Peak-Signal-to-Noise Ratio (PSNR) by 0.16 dB compared to the benchmark methods.

Emotional State as a Key Driver of Public Preferences for Flower Color

Flowers, as integral elements of urban landscapes, are critical not only for aesthetic purposes but also for fostering human–nature interactions in green spaces. However, research on flower color preferences has largely been descriptive, and there is a lack of exploration of potential mechanisms influencing flower color preferences, such as economic and social factors. This study created visual samples through precise color adjustment techniques and introduced the L*, a*, and b* parameters from the CIELAB color system to quantify the flower colors of the survey samples, conducting an online survey with 354 Chinese residents. The complex aesthetic process’s driving factors were unveiled through a comprehensive analysis using a Generalized Additive Model (GAM), a piecewise Structural Equation Model (SEM), and linear regression models. The results show that the public’s flower color preference is primarily related to the a* and b* parameters, which represent color dimensions in the CIELAB color space, and it is not significantly related to L* (lightness). Factors such as age, annual household income level (AI), personal income sources (PI), nature experience, and emotional state (TMD) significantly influence color preferences, with emotional state identified as the most critical factor. Lastly, linear regression models further explain the potential mechanism of the influencing factors. This study proposes a framework to assist urban planners in selecting flower colors that resonate with diverse populations, enhancing both the attractiveness of urban green spaces and their potential to promote pro-environmental behavior. By aligning flower color design with public preferences, this study contributes to sustainable urban planning practices aimed at improving human well-being and fostering deeper connections with nature.

Deep Learning Classification and Object Detection in Helicopter Images: Performance Analysis of GoogleNet, AlexNet and YOLOv9c Architectures

Helicopter imaging classification and detection are crucial for autonomous navigation, military operations, search and rescue, and civil aviation management. This study utilized two helicopter image datasets, applying data augmentation techniques such as random resizing, cutting, horizontal rotation, rotation, and color adjustments, along with histogram equalization for contrast enhancement. Twenty-four helicopter classes were trained using GoogleNet and AlexNet architectures, while the YOLOv9c model was employed for object detection. The results revealed that the GoogleNet classification model achieved an 81% F1 score, and AlexNet reached 73%. In contrast, the YOLOv9c model demonstrated an average mean Average Precision (mAP) of 87%. These findings indicate that CNN architectures and YOLO are effective for helicopter image classification and detection, highlighting their potential applications in military, search and rescue, and civil aviation contexts.

A complete workflow from embalmed specimens to life-like 3D virtual models for veterinary anatomy teaching.

Understanding normal structural and functional anatomy is critical for health professionals across various fields such as medicine, veterinary, and dental courses. The landscape of anatomical education has evolved tremendously due to several challenges and advancements in blended learning approaches, which have led to the adoption of the use of high-fidelity 3D digital models in anatomical education. Cost-effective methods such as photogrammetry, which creates digital 3D models from aligning 2D photographs, provide a viable alternative to expensive imaging techniques (i.e. computed tomography and magnetic resonance imaging) whilst maintaining photorealism and serving multiple purposes, including surgical planning and research. This study outlines a comprehensive workflow for producing realistic 3D digital models from embalmed veterinary specimens. The process begins with the preservation of specimens using the modified-WhitWell (WhitWell-Liverpool) embalming protocol, which ensures optimal tissue rigidity and improved colour enhancement, facilitating easier manipulation and better photogrammetry outcomes. Once embalmed, specimens are photographed to create digital 3D models using photogrammetry. Briefly, all images are processed to generate a sparse point cloud, which is then rendered into a 3D mesh. The mesh undergoes decimation and smoothing to reduce computational load, and a texture is applied to create a lifelike model. Additional colour enhancements and adjustments are made using digital tools to restore the natural appearance of the specimens. The 3D models are stored on a cloud repository and integrated into the University of Liverpool's Virtual Learning Environment, providing continuous, remote access to high-quality anatomical resources. The switch to embalmed specimens during the COVID-19 pandemic allowed for longer-term use and detailed dissections, enhancing the quality of digital models. Fresh specimens, though naturally coloured, are less stable for photogrammetry, making embalmed specimens preferable for accurate 3D modelling. Our method ensures embalmed specimens are rigid enough for precise modelling while allowing texture adjustments to enhance digital representation. This approach has improved logistical efficiency, educational delivery, and specimen quality. Innovative embalming techniques and advanced photogrammetry have the power to revolutionise anatomical education with the creation of a vast digital library accessible online to students at any time. This approach paves the way for integrating digital 3D models into immersive environments and assessing their impact on learning outcomes.

Single-Component Coordination Polymers with Excitation Wavelength- and Temperature-Dependent Long Persistent Luminescence toward Multilevel Information Security.

Metal-organic hybrid materials with long persistent luminescence (LPL) properties have attracted a lot of attention due to their enormous potential for applications in information encryption, anticounterfeiting, and other correlation fields. However, achieving multimodal luminescence in a single component remains a significant challenge. Herein, we report two two-dimensional LPL coordination polymers: {[Zn2(BA)2(BIMB)2]·2H2O}n (1) and {[Cd(BA)(BIMB)]·3H2O}n (2) (BIMB = 1,3-bis(imidazol-1-yl)benzene; BA = butanedioic acid). Their LPL colors can be adjusted by the excitation wavelength or temperature variation in a single-component coordination polymer, achieving multimode color adjustment from green to orange or blue to yellow. X-ray single-crystal diffraction analysis and theoretical calculations demonstrate that abundant intermolecular interactions, ligand-to-ligand charge transfer (LLCT) transitions, and heavy atom effects of the central metal can realize multicolor afterglow. This work provides a convenient strategy for new pattern multicolor LPL materials and may also inspire new ideas for advanced information encryption technologies.

Visualizing supramolecular assembly behavior, stimulus response, and solid state emission of higher-order Pt2+ aggregates.

Here, we present the first instance of a highly efficient red tetramer aggregate with tunable emission based on a cationic platinum(II) complex in conjunction with a silver cluster anion counterpart. This system exhibits multicolor emission response behaviors, which can be conveniently and directly detected through spectroscopic analysis, showcasing intriguing luminescence changes. The self-assembly of Pt⋯, π-π, and hydrogen bonding interactions not only enables an intriguing color adjustment from green to yellow emission, and eventually to red emission, but also demonstrates the co-existence of the monomer, excimer, and aggregation. These phenomena are further accompanied by well-defined nanostructures. The self-assembly process of these structures exhibits an isodesmic growth mechanism, which is dependent on temperature. In this regard, it exhibits potential applicability in multi-mode logic gates that rely on external stimuli such as concentration, solvent, and temperature. The sensitivity of the aggregates towards chemical stimuli combined with their exceptionally bright emission characteristics renders them suitable for diverse applications including solid-state lighting sensing mechanisms and anticounterfeiting measures. The multi-stimuli responsive phosphorescence and self-assembly behaviors of the cationic platinum(II) complex were substantiated by X-ray crystal structure determination, 1H NMR analysis spectroscopic investigations, computational calculations and scanning electron microscopy (SEM) studies.

Surface Roughness and Color Stability of Newly Developed Resin Composites With Color Adjustment Potential After Immersion in Staining Solutions.

The aim of this this study was to evaluate the surface roughness and color stability of resin composites (RCs) with color adjustment potential (CAP): Omnichroma (OM), (Tokuyama Dental, Tokyo, Japan); Neo Spectra ST (NS) (Dentsply Sirona, York, PA,USA); and Charisma Diamond One (CD) (Kulzer, Hanau, Germany), compared to a conventional RC, Filtek Z350XT (FZ) (3M ESPE, St. Paul, MN,USA), after immersion in three staining solutions, cola, coffee, and black tea, with distilled water as a control. Forty specimens (8 mm diameter x 2 mm thickness) were prepared for each RC material and divided into subgroups (n=10/group) based on the immersion solution. Surface roughness was measured at baseline and after 21 days of immersion using a non-contact optical profilometer. Color change (ΔE00) was evaluated at baseline, 1, 7, and 21 days with a spectrophotometer. The results showed that FZ demonstrated the lowest surface roughness (0.14), significantly differing from that of the other RCs with CAP (p<0.05), while no significant differences in surface roughness were observed between RCs with CAP (p>0.05). There were no significant changes in surface roughness before and after immersion in various solutions. The results for color change (ΔE00) were statistically significant. Neo Spectra ST showed the lowest tendency for color change, while Filtek Z350XT displayed a moderate potential, and Omnichroma and Charisma Diamond One showed the highest potential for color change.

Aplikasi Website dengan Flask dan Open CV untuk Filtering Warna Bagi Penderita Buta Warna

Color blindness is a hereditary vision disorder that impairs the ability to distinguish certain colors, significantly affecting daily activities and quality of life. This study aims to develop a web-based application using Flask and OpenCV to assist individuals with color blindness in identifying colors accurately. The application incorporates image processing technology to enhance color contrast and simulate real-time color perception adjustments. Employing the Waterfall model of the Software Development Life Cycle (SDLC), the study encompasses requirements analysis, system design, implementation, testing, and maintenance. Key features include Camify, for real-time color adjustments via device cameras, and Pickerify, for detecting colors in uploaded or live images. Testing reveals the application's effectiveness in providing improved color perception for users with various types of color blindness (e.g., Deuteranopia, Protanopia, Tritanopia). Despite minor limitations under extreme lighting conditions, the intuitive user interface and robust functionality make the application accessible to diverse user groups. Future enhancements include integrating AI for personalized filters and expanding compatibility with emerging technologies.

Fluorescence and color adjustment potentials of paste-type and flowable resin composites in cervical restorations.

Comparatively assess the fluorescence and color adjustment potentials of paste-type and flowable resin composites performed in cervical restorations. Five paste-type resin composites and 5 highly-filled flowable composites were investigated (n=5 for each). Class V cavities on 50 extracted A2 shade human central incisors were restored. Cross-polarization (CP) and fluorescence illumination (FI) images were recorded for each restoration. L*a*b* values were analyzed using the Photoshop software and color adjustment (ΔECP) and fluorescence adjustment (ΔEFI) levels were quantitatively assessed. Kruskal Wallis Test, Independent Samples t Test, One Way Anova-Welch test, and Pearson Correlation were used for the statistical analyses. The deemed significance was set at p<.050. Paste-type composites presented significantly lower ΔEFI and ΔECP values than the flowable composites (p=.006 and p<.001 respectively). ΔEFI and ΔECP values were positively correlated (r=0.584; p<.001). Paste-type Gaenial A'chord presented the lowest ΔEFI value [7.67 (5.35 - 10.99)b], while Estelite Asteria presented significantly the highest [19.94 (17.08 - 22.17)c]. Charisma Diamond One Flow presented the highest ΔECP value [3.25 (3.13 - 3.39)a] and Gaenial A'chord presented the lowest [0.79 (0.48 - 0.87)e] which was the only imperceptible score (<0.8). The only clinically acceptable color adjustment was found for Geanial Universal Injectable [1.06 (0.73 - 1.62)e] among the flowable composites. ΔEFI and b* values were positively correlated (r=0.493; p<.001). Color adjustment level and fluorescence adjustment level were considered coherent for particular composite brands. At the same time, both were better for the paste-type composites than the flowable composites on A2 base shade class V restorations. Fluorescence adjustment potential as well as the color adjustment potential varied among different composite brands regarding paste-type and flowable composites. Understanding both the color and fluorescence adjustment potentials of paste-type and flowable resin-based composite materials is clinically vital to achieve the high-end esthetics.

Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting Conditions

The visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.

Seamless and Aligned Texture Optimization for 3D Reconstruction

AbstractRestoring the appearance of the model is a crucial step for achieving realistic 3D reconstruction. High‐fidelity textures can also conceal some geometric defects. Since the estimated camera parameters and reconstructed geometry usually contain errors, subsequent texture mapping often suffers from undesirable visual artifacts such as blurring, ghosting, and visual seams. In particular, significant misalignment between the reconstructed model and the registered images will lead to texturing the mesh with inconsistent image regions. However, eliminating various artifacts to generate high‐quality textures remains a challenge. In this paper, we address this issue by designing a texture optimization method to generate seamless and aligned textures for 3D reconstruction. The main idea is to detect misalignment regions between images and geometry and exclude them from texture mapping. To handle the texture holes caused by these excluded regions, a cross‐patch texture hole‐filling method is proposed, which can also synthesize plausible textures for invisible faces. Moreover, for better stitching of the textures from different views, an improved camera pose optimization is present by introducing color adjustment and boundary point sampling. Experimental results show that the proposed method can eliminate the artifacts caused by inaccurate input data robustly and produce high‐quality texture results compared with state‐of‐the‐art methods.

Cavosurface Margin Design on Color Adjustment of Resin Composites

Cavosurface Margin Design on Color Adjustment of Resin Composites

사진 편집 앱의 색상 조정 기능의 효율성 향상을 위한 UI 디자의 비교분석 연구 : VSCO, Hypic, Lightroom, Snapseed를 중심으로

With the development of smartphones, there are more and more kinds of functions, among which the most used by users is the camera.In fact, Some smartphones even use the photo function as the main means of promotion. As smartphone cameras advance, so does their popularity. A key feature in photo editing apps is color adjustment, which lets users tweak the mood and style of their images. A well-crafted UI is crucial for making this feature efficient, affecting how easily users can find and use it, and their overall visual and interactive experience. This study zeroes in on the UI design of color adjustment in four top photo editing apps: VSCO, Hypic, Lightroom, and Snapseed. The findings are straightforward: First, there's a clear variation in how these apps navigate, design icons, and lay them out. Second, each app excels in organizing content and has its own distinct flair. Third, by examining their weaknesses and conducting market research, the study offers suggestions to enhance UI efficiency for color adjustment. Finally, it is hoped that this will be helpful to mobile platforms that want to create an overall user experience that allows users to quickly and accurately use the features they need and are accustomed to.

YOLOv8-Based Drone Detection: Performance Analysis and Optimization

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study is the evaluation of deep learning approaches such as pre-trained YOLOv8 drone detection for security issues. This study focuses on the YOLOv8 model to achieve optimal performance in object detection tasks using a publicly available dataset collected by Mehdi Özel for a UAV competition that is sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. YOLOv8, known for its advanced architecture, was selected due to its suitability for real-time detection applications and its ability to process complex visual data. Hyperparameter tuning and data augmentation techniques were applied to maximize the performance of the model. Basic hyperparameters such as learning rate, batch size, and optimization settings were optimized through iterative experiments to provide the best performance. In addition to hyperparameter tuning, various data augmentation strategies were used to increase the robustness and generalization ability of the model. Techniques such as rotation, scaling, flipping, and color adjustments were applied to the dataset to simulate different conditions and variations. Among the augmentation techniques applied to the specific dataset in this study, rotation was found to deliver the highest performance. Blurring and cropping methods were observed to follow closely behind. The combination of optimized hyperparameters and strategic data augmentation allowed YOLOv8 to achieve high detection accuracy and reliable performance on the publicly available dataset. This method demonstrates the effectiveness of YOLOv8 in real-world scenarios, while also highlighting the importance of hyperparameter tuning and data augmentation in increasing model capabilities. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605, and a considerable precision–recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method

Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images.

A CNN based model for heart disease detection

Cardiovascular diseases (CVDs) pose a formidable global health challenge, claiming millions of lives annually. Despite advancements in healthcare, heart disease remains a leading cause of mortality, especially in developing nations. Early detection of cardiac abnormalities through predictive models is crucial for effective intervention. This research leverages machine learning (ML) and artificial intelligence (AI), focusing on deep learning, to enhance diagnostic capabilities. Unlike previous studies, this work introduced caffeine as a potential risk factor often overlooked in datasets. The study utilized Magnetic Resonance Imaging (MRI) datasets from Enugu State University Teaching Hospital and Kaggle, comprising 90,500 samples, with specific attention to high caffeine intake cases. Data preprocessing involved resizing, normalization, color adjustments, and augmentation to optimize model training. A Convolutional Neural Network (CNN) architecture with four convolutional layers was employed for classification. The CNN model achieved a remarkable accuracy of 94% and low loss values, and demonstrated proficiency in categorizing heart MRI data. Ten-fold cross-validation reaffirmed the model's high success rate with an average accuracy of 94.13% and minimal loss function. Comparative analysis showcased the effectiveness of the developed CNN model, outperforming several existing models in heart disease classification.

Optimized automated blood cells analysis using Enhanced Greywolf Optimization with integrated attention mechanism and YOLOv5

Blood testing is widely regarded as a fundamental diagnostic procedure in healthcare, with blood cell counting being particularly vital for diagnosing conditions and evaluating overall health. Traditional methods employing hemocytometers and diverse laboratory instruments are typically employed to conduct blood counts manually. Using a microscope, manually counting, and examining blood cells takes time and effort. Thus, developing autonomous blood cells detecting and counting cells in this system becomes necessary to help doctors diagnose patients quickly and accurately. The proposed method integrates the YOLOv5 object detection framework with Enhanced Graywolf Optimization (EGWO) and an Attention Mechanism to improve the accuracy and efficiency of RBC detection. The YOLOv5 architecture is optimized for the task through the EGWO algorithm, which fine-tunes the model parameters, while the Attention Mechanism focuses on extracting critical features from blood cell images. The model is trained and validated using the BCCD dataset, employing an 80/20 split for training and validation. Data augmentation techniques, such as random rotations, flips, and color adjustments, are applied to enhance model generalization. Experimental results demonstrate that our method achieves a high accuracy of 99.89 %, outperforming existing models like Faster R-CNN, CNN, and DCNN regarding accuracy, training time, and inference speed. The proposed method's moderate complexity and fast inference time make it suitable for real-time applications in clinical settings. This research provides a robust and efficient solution for automated RBC detection and counting, with potential implications for improving diagnostic processes in hematological analyses. Overall, in practical applications, it is helpful for counting red blood cells from smeared pictures in less than a second.

Product Colour Variation Management with Artificial Intelligence

This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace by incorporating artificial intelligence, deep learning, and neural network systems. Real-time consumer and product information, preferences, buying history, and sales history; I created an AI model to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestions for Businesses adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in ways of enhancing futuristic consumer-oriented marketing approaches. The paper was first completed in 2021 and later I have modified the paper with latest updates till date 2024.

Local color display technology

AbstractThe local color display technology utilizes a high refresh rate LCD open‐cell without a color filter and enables color adjustment through independent RGB backlight zones isolated by an anti‐scatter grid (ASG). Synchronized calibration between the open‐cell and the backlight zones is achieved by aligning the backlight controller with the vertical clock pulse (CPV)/vertical start pulse (STV) signals in the open‐cell gate driver, thereby determining the open‐cell scan line position. Once synchronous calibration is completed, the backlight controller refreshes the corresponding RGB backlight zones whenever the open‐cell scan line passes through them. The local color display technology can reduce the cost of open‐cell by 20%, improve resolution by three times, enhance brightness by eight times, expand the color gamut to BT2020, and improve grayscale to 32‐bits.

Visual evaluation of the color adjustment of single-shade and group shade resin composites in restorations with different cavity configurations

ObjectiveTo evaluate the influence of the tooth shade, type of resin composite and cavity configuration on the color adjustment of single-shade and group shade resin composites. MethodsClass I and V cavities were prepared in artificial acrylic mandibular right molars with different shades - A1, A2, A3, A3.5 and A4. Three single-shade (OMN, UNI, CHA) and two group shade (FIL, HAR) resin composites were evaluated. Seventy-five observers, including dentists, dental students and laypersons (n = 25), participated in the study. A psychophysical experiment based on visual assessments of the color matching between each tooth and each restoration was ranked from 0 (excellent match) to 4 (huge mismatch), and mean frequencies (%) were calculated. Visual color differences among composite materials and tooth shades were statistically tested (P < 0.05). ResultsThe results showed highest percentages of unacceptable color mismatches for class V compared to class I restorations, especially for more chromatic tooth shades (A3.5 and A4). The percentage of ratings of acceptability was higher for laypersons group. For class V, OMN presented significant difference only from UNI in tooth shades A1, A2, A3 and A4. However, for class I, OMN presented significant differences with CHA for A1, A2 and A4, and with HAR and FIL for A1. ConclusionsClass I resin composite restorations exhibit superior color matching than Class V, with lighter and low chroma tooth shades demonstrating better color adjustment. The color matching of both cavity configurations is comparable for single and group shade resin composites. Clinical significanceCavity configuration and tooth shade may impact color matching of single and group shade resin composites. This study demonstrates superior color matching in class I configurations over class V for both single and group shade resin composites. Additionally, restorations placed in teeth with lighter shades exhibit more favorable color adjustment.