Linghui Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Linghui Zhang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Feng Li
School of Architecture and Art, Taiyuan University of Technology, Jinzhong, Shanxi, China
1 shared publication - Shuyuan Xue
School of Architecture and Art, Taiyuan University of Technology, Jinzhong, Shanxi, China
1 shared publication - Xuanyue Zhao
School of Architecture and Art, Taiyuan University of Technology, Jinzhong, Shanxi, China
1 shared publication - Xukai Chai
School of Architecture and Art, Taiyuan University of Technology, Jinzhong, Shanxi, China
1 shared publication - Yan Hao
School of Architecture and Art, Taiyuan University of Technology, Jinzhong, Shanxi, China
1 shared publication - Zeyuan Li
School of Architecture and Art, Taiyuan University of Technology, Jinzhong, China
1 shared publication
Latest Publications
- Open Access
- 6 February 2026
- Published by Association for Computing Machinery (ACM)
- p. 71-76
Multi-scale Separation and Progressive Restoration of Northern Qi Xu Xianxiu Murals from Structure to Color
As a significant historical and cultural heritage, the murals of Xu Xianxiu from the Northern Qi Dynasty have long faced complex deterioration including structural ambiguity, partial loss, and chromatic degradation. Traditional single-model restoration approaches (e.g., relying solely on super-resolution or image restoration models) often struggle to simultaneously address multidimensional challenges such as structural ambiguity, local missing data, and color degradation, frequently resulting in imbalanced outcomes in structural integrity, semantic coherence, or chromatic fidelity. Current research predominantly focuses on single-degradation-type restoration, lacking a systematic and progressive framework for tackling the multi-scale composite degradation issues in murals.To address this, this paper proposes a progressive mural restoration framework based on multi-scale separation strategy. Through task- decomposition and modular pipeline design, three deep learning models—Real-ESRGAN, LaMa, and Zero-DCE—are sequentially employed to accomplish large-scale structural reconstruction, local semantic restoration, and global color recovery. Experiments using simulated and authentic mural data were evaluated through quantitative metrics (PSNR, SSIM, FID) and visual perception. Results demonstrate that this progressive workflow significantly enhances restoration quality, achieving notable improvements in structural clarity, content integrity, and color authenticity. The key contributions of this study are as follows: First, we propose a multi-scale separation-based progressive mural restoration framework that decomposes complex restoration tasks into three subtasks: structural reconstruction, local restoration, and color restoration. Second, we develop a modular restoration workflow integrating Real-ESRGAN, LaMa, and Zero-DCE to achieve systematic restoration from structural to semantic and color levels. Third, we design a physical degradation simulation method and construct a mural dataset combining simulated and real-world data. Finally, quantitative and qualitative experiments demonstrate significant improvements in structural clarity, content integrity, and color authenticity achieved by our framework.
- 6 February 2026
- Published by Association for Computing Machinery (ACM)
- p. 71-76
Open AccessMulti-scale Separation and Progressive Restoration of Northern Qi Xu Xianxiu Murals from Structure to Color
As a significant historical and cultural heritage, the murals of Xu Xianxiu from the Northern Qi Dynasty have long faced complex deterioration including structural ambiguity, partial loss, and chromatic degradation. Traditional single-model restoration approaches (e.g., relying solely on super-resolution or image restoration models) often struggle to simultaneously address multidimensional challenges such as structural ambiguity, local missing data, and color degradation, frequently resulting in imbalanced outcomes in structural integrity, semantic coherence, or chromatic fidelity. Current research predominantly focuses on single-degradation-type restoration, lacking a systematic and progressive framework for tackling the multi-scale composite degradation issues in murals.To address this, this paper proposes a progressive mural restoration framework based on multi-scale separation strategy. Through task- decomposition and modular pipeline design, three deep learning models—Real-ESRGAN, LaMa, and Zero-DCE—are sequentially employed to accomplish large-scale structural reconstruction, local semantic restoration, and global color recovery. Experiments using simulated and authentic mural data were evaluated through quantitative metrics (PSNR, SSIM, FID) and visual perception. Results demonstrate that this progressive workflow significantly enhances restoration quality, achieving notable improvements in structural clarity, content integrity, and color authenticity. The key contributions of this study are as follows: First, we propose a multi-scale separation-based progressive mural restoration framework that decomposes complex restoration tasks into three subtasks: structural reconstruction, local restoration, and color restoration. Second, we develop a modular restoration workflow integrating Real-ESRGAN, LaMa, and Zero-DCE to achieve systematic restoration from structural to semantic and color levels. Third, we design a physical degradation simulation method and construct a mural dataset combining simulated and real-world data. Finally, quantitative and qualitative experiments demonstrate significant improvements in structural clarity, content integrity, and color authenticity achieved by our framework.