Context-Encoder-Based Image Inpainting for Ancient Chinese Silk

Abstract

The rapid advancement of deep learning technologies presents novel opportunities for restoring damaged patterns in ancient silk, which is pivotal for the preservation and propagation of ancient silk culture. This study systematically scrutinizes the evolutionary trajectory of image inpainting algorithms, with a particular emphasis on those firmly rooted in the Context-Encoder structure. To achieve this study’s objectives, a meticulously curated dataset comprising 6996 samples of ancient Chinese silk (256 × 256 pixels) was employed. Context-Encoder-based image inpainting models—LISK, MADF, and MEDFE—were employed to inpaint damaged patterns. The ensuing restoration effects underwent rigorous evaluation, providing a comprehensive analysis of the inherent strengths and limitations of each model. This study not only provides a theoretical foundation for adopting image restoration algorithms grounded in the Context-Encoder structure but also offers ample scope for exploration in achieving more effective restorations of ancient damaged silk.

Keywords

• ancient Chinese silk
• deep learning
• image inpainting
• context-encoder-based model
• mask awareness