Abstract:

Objective In order to solve problems of the inefficiencies, high costs, and limited variety in conventional handmade fabric pattern color designs, which are worsened by the frequent shifts in seasonal fashion colors, an efficient, intelligent color-transfer method for fabric patterns is introduced, based on the latest fashion color trends. By closely following fashion color trends, it aims to keep textile and apparel products visually appealing and appealing to consumers. Ultimately, this approach can minimize waste from color-related obsolescence and help the industry quickly adapt to market changes.

Method A comprehensive and trend-responsive color palette was developed by integrating fashion color trends, color theory principles, and the unique color attributes of fabric patterns. Using the K-means clustering algorithm, a dataset of fabric patterns incorporating popular color palettes was established. In order to refine the color transfer model, an edge-preserving loss function and a color perception loss function were introduced. This enhanced the ability of the model to capture intricate texture details while maintaining color authenticity. Additionally, salt-and-pepper noise during the model training phase was incorporated to enhance the overall robustness of the model.

Results A well-designed survey with 40 participants was conducted to compare two different color palettes in detail. One palette incorporated complex features of fabric patterns, while the other served as a control without these elements. Participants were asked to select their preferred palette based on various factors such as overall fabric design, pattern complexity, and color harmony. This evaluation, based on a diverse set of 90 test samples, ensured the comprehensiveness of the assessment. The results showed that the palette incorporating fabric pattern features received overwhelming positive feedback, with 2 425 votes (67.36% of the total), significantly outperforming the conventional palette and highlighting the strong appeal of fabric-inspired designs. Furthermore, the proposed color transfer model demonstrated excellent performance metrics. Specifically, it achieved a structural similarity index (s) of 0.911, indicating high visual fidelity between the original and transferred images. Additionally, the peak signal-to-noise ratio (p) reached 29.714, further confirming the model's ability to maintain image quality during the color transfer process. Compared to other conventional color transfer methods that perform well.It outperformed Neural Preset (p=28.295,s=0.894) and AdaIN (s=0.857), These results validate its strengths in maintaining color authenticity and structural fidelity for floral pattern adaptation.

In order to verify the contributions of the introduced salt-and-pepper noise, edge loss, and color perception loss to optimizing the color transfer model, ablation experiments were conducted. Compared to the baseline (p=27.596, s=0.806), introducing color perception loss alone (p=28.621, s=0.892) improved color accuracy by 3.7% in p and 10.7% in s, demonstrating superior sensitivity to subtle hue variations. Edge loss achieved an SSIM of 0.844 (4.7% gain), effectively preserving structural details, while salt-and-pepper noise enhanced robustness with a 0.9% p increase (27.835). The integrated model combining all components achieved optimal performance (p=29.714, s=0.911). The results indicated that the introduction of the color perception loss function significantly improved the model's sensitivity to subtle color changes, resulting in more precise and accurate matching of target fashion colors. Thirdly, the edge-preserving loss function played a crucial role in mitigating edge blurring and detail loss during the color transfer process, ensuring that the resulting patterns maintained clear outlines and rich details. Finally, incorporating salt-and-pepper noise during the training phase enhanced the model's robustness to fluctuations in image quality, simulating real-world disturbances that may occur during image transmission or storage. This enhancement allowed the model to accurately distinguish between true structures and noise, ultimately generating more stable and visually appealing images.

Conclusion The proposed fabric pattern color transfer method based on popular colors significantly improves design efficiency and enriches the diversity of fabric patterns while effectively reducing production costs through intelligent means. The feasibility and effectiveness of this method have been verified through empirical experiments. The proposed color transfer method provides an efficient solution for the intelligent design of fabric patterns, accelerating the digital transformation of the textile and apparel industry. Furthermore, by applying the color transfer technology to the fusion design of popular colors and fabric patterns, it not only meet the market's demand for diversification and personalization but also further respond to the initiative of sustainable development. This approach helps reduce dependence on new materials, aligns with the principles of a circular economy, and provides a more sustainable solution for the textile industry. Ultimately, the research lays the foundation for achieving textile designs that are both environmentally friendly and aesthetically pleasing, making a positive contribution to promoting a sustainable, innovative, and vibrant fashion industry.

Key words: color transfer, generative adversarial network, fabric pattern, fabric pattern design, fashion color