A Modular Deep Learning Pipeline (CNN+U-Net+GAN) for Color-Accurate, Cross-Material Digital Textile Printing with Transfer-Learning-Based Material Adaptation

Abstract

Precise color reproduction and efficient pattern generation are the core goals of digital printing on clothing. To break through the limitations of traditional processes that rely on manual parameter adjustment and sample fabric trial and error, this paper proposes an intelligent printing generation framework based on deep learning. This framework integrates CNN color management, deep segmentation and loop optimization, GAN-driven 3D virtual rendering and transfer learning material adaptation, and can achieve end-to-end pattern generation and computational optimization on multi-material data such as cotton fabric, silk and polyester. The system not only captures the spatial detail features of the patterns (such as edge sharpness and color gradation), but also maintains color consistency and detail restoration among different materials through cross-domain modeling. The experimental results show that on multi-material datasets, this scheme achieves ΔE 1.9±0.2across cotton/silk/polyester (mean over 3 runs), which corresponds to a 30–45% reduction versus screen printing (ΔE≈4.1) and 15–25% versus a commercial inkjet baseline (ΔE≈2.3). It reduces splicing fracture rate to <4%, shortens average processing time by ~60% (12 h→4.8–8.5 h depending on batch size), and increases SSIM to 0.93±0.01.All statistics are mean±std over three independent runs; significance is assessed with paired t-tests or ANOVA with Bonferroni correction at α=0.05. This research not only verified the effectiveness of deep learning in digital printing, but also provided an expandable intelligent path for the integration of the clothing design and production chain, offering significant support for the transformation of the fashion industry towards personalization, greenness and intelligence.