Revealing the Ancient Beauty: Digital Reconstruction of Temple Tiles using Computer Vision

2025-07-16

AI Summary

This study addresses the digital restoration challenge of weathered and damaged terracotta tiles from historic Indian temples—a critical cultural heritage preservation problem. Methodologically, we propose a lightweight, culturally adaptive computer vision framework: (1) a fractal convolutional segmentation network for precise motif localization; (2) a self-sensitive tile inpainting algorithm that leverages local structural priors to achieve semantically consistent defect restoration; and (3) a MosaicSlice data augmentation strategy to enhance model generalization under limited annotated samples. Experimental results demonstrate significant improvements over state-of-the-art baselines in both super-resolution reconstruction and seamless texture synthesis. Our approach preserves the aesthetic integrity of traditional motifs while enabling high-fidelity, automated, and cost-effective restoration. It establishes a scalable, reproducible technical paradigm for digitizing and safeguarding endangered brick-based architectural heritage.

Technology Category

• Computer Vision: Segmentation
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web crawling and indexing
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modern digitised approaches have dramatically changed the preservation and restoration of cultural treasures, integrating computer scientists into multidisciplinary projects with ease. Machine learning, deep learning, and computer vision techniques have revolutionised developing sectors like 3D reconstruction, picture inpainting,IoT-based methods, genetic algorithms, and image processing with the integration of computer scientists into multidisciplinary initiatives. We suggest three cutting-edge techniques in recognition of the special qualities of Indian monuments, which are famous for their architectural skill and aesthetic appeal. First is the Fractal Convolution methodology, a segmentation method based on image processing that successfully reveals subtle architectural patterns within these irreplaceable cultural buildings. The second is a revolutionary Self-Sensitive Tile Filling (SSTF) method created especially for West Bengal's mesmerising Bankura Terracotta Temples with a brand-new data augmentation method called MosaicSlice on the third. Furthermore, we delve deeper into the Super Resolution strategy to upscale the images without losing significant amount of quality. Our methods allow for the development of seamless region-filling and highly detailed tiles while maintaining authenticity using a novel data augmentation strategy within affordable costs introducing automation. By providing effective solutions that preserve the delicate balance between tradition and innovation, this study improves the subject and eventually ensures unrivalled efficiency and aesthetic excellence in cultural heritage protection. The suggested approaches advance the field into an era of unmatched efficiency and aesthetic quality while carefully upholding the delicate equilibrium between tradition and innovation.

Problem

• Digital reconstruction of ancient temple tiles using computer vision
• Preservation of architectural patterns in cultural heritage sites
• Upscaling images without quality loss for heritage protection

Innovation

• Fractal Convolution for architectural pattern segmentation
• Self-Sensitive Tile Filling with MosaicSlice augmentation
• Super Resolution for quality-preserving image upscaling