Paper
3 January 2025 Based on structure and texture dual-stream network for ancient mural restoration
Chunhong Ren, Yan Xu
Author Affiliations +
Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134420L (2025) https://doi.org/10.1117/12.3052938
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China
Abstract
Presently although the algorithms based on deep learning have achieved good results in the restoration of ancient murals, they do not consider the interaction between image texture information and structural information, resulting in the ineffective restoration of global information and prone to problems such as discontinuous structures and blurry textures. To address these issues, this paper proposes an ancient mural restoration algorithm based on structure and texture-guided dual-stream generative adversarial networks. Between encoding and decoding, an improved aggregated contextual-transformation (IAOT) module is proposed to enhance the capture of distant features and rich structural details. Experimental results show that the proposed method outperforms comparative algorithms in terms of restoring image texture details and structures, and performs better in evaluation metrics.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Chunhong Ren and Yan Xu "Based on structure and texture dual-stream network for ancient mural restoration", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134420L (3 January 2025); https://doi.org/10.1117/12.3052938
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KEYWORDS
Image restoration

Image enhancement

Image quality

Education and training

Feature extraction

Distortion

Feature fusion

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