Digital color enhancement in ceramic imagery using graph-guided residual learning and adaptive scattering models

Abstract

With the advancement of digital technologies, accurate color extraction and restoration in ceramic image processing have become pivotal in preserving cultural heritage and enhancing digital design applications. This study presents a novel ceramic image denoising framework based on a deep learning algorithm tailored for digital color enhancement. The proposed architecture integrates a densely connected residual network, a contrast-based graph generator, and an improved adaptive light scattering model. The residual modules capture multi-scale texture features, while the graph generator enhances structural detail learning by incorporating spatial saliency. The light scattering model adaptively fuses texture and color information to restore high-fidelity ceramic images. Extensive experiments on the Chinese Neolithic painted pottery and ARCA328 datasets show that the proposed method consistently surpasses prior approaches. Compared with the strongest baseline (PFF-Net), it achieves a 51.7% improvement in PSNR, a 31% increase in SSIM, as well as a 27.8% reduction in MSE and a 40.6% reduction in MAE. Furthermore, applying the method as a preprocessing step for segmentation tasks notably increases segmentation accuracy, highlighting its practical value in downstream ceramic image analysis. This work provides a robust foundation for intelligent ceramic modeling and digital preservation through enhanced visual quality.