Scholarlink

<p>Cultural relic image restoration presents unique challenges due to irregular damage and historically specific textures, which standard deep learning methods struggle to address. This paper proposes a novel two-stage Transformer-CNN framework tailored for this task. The first stage leverages a Transformer to capture global structural dependencies from low-resolution priors, generating coherent coarse proposals. The second stage employs a specialized CNN to refine fine-grained textures from these proposals, optimized by a compound perceptual loss function. Validated on a new large-scale dataset of 88,000 East Asian cultural relic images, our approach demonstrates state-of-the-art performance. A key contribution is the generation of diversified restoration outputs, providing conservators with multiple valid references for decision-making. This work establishes an effective paradigm for digital heritage conservation that balances global structural integrity with local texture fidelity.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cultural relic image restoration using two-stage transformer-CNN framework

  • 期刊论文

  • 发表日期：

    2025年12月27日

查看全文
  • Xing Wu,
  • Deyu Gao,
  • Zhi Li,
  • Junfeng Yao,
  • Quan Qian,
  • Jun Song

摘要

Cultural relic image restoration presents unique challenges due to irregular damage and historically specific textures, which standard deep learning methods struggle to address. This paper proposes a novel two-stage Transformer-CNN framework tailored for this task. The first stage leverages a Transformer to capture global structural dependencies from low-resolution priors, generating coherent coarse proposals. The second stage employs a specialized CNN to refine fine-grained textures from these proposals, optimized by a compound perceptual loss function. Validated on a new large-scale dataset of 88,000 East Asian cultural relic images, our approach demonstrates state-of-the-art performance. A key contribution is the generation of diversified restoration outputs, providing conservators with multiple valid references for decision-making. This work establishes an effective paradigm for digital heritage conservation that balances global structural integrity with local texture fidelity.