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Journal Article 이미지 생성 및 지도학습을 통한 전통 건축 도면 노이즈 제거
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Authors
최낙관, 이용식, 이승재, 양승준
Issue Date
2022-02
Citation
건축역사연구, v.31, no.1, pp.41-50
ISSN
1598-1142
Publisher
한국건축역사학회
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.7738/JAH.2022.31.1.041
Abstract
Traditional wooden buildings deform over time and are vulnerable to fire or earthquakes. Therefore, traditional wooden buildings require continuous management and repair, and securing architectural drawings is essential for repair and restoration. Unlike modernized CAD drawings, traditional wooden building drawings scan and store hand-drawn drawings, and in this process, many noise is included due to damage to the drawing itself. These drawings are digitized, but their utilization is poor due to noise. Difficulties in systematic management of traditional wooden buildings are increasing. Noise removal by existing algorithms has limited drawings that can be applied according to noise characteristics and the performance is not uniform. This study presents deep artificial neural network based noised reduction for architectural drawings. Front/side elevation drawings, floor plans, detail drawings of Korean wooden treasure buildings were considered. First, the noise properties of the architectural drawings were learned with both a cycle generative model and heuristic image fusion methods. Consequently, a noise reduction network was trained through supervised learning using training sets prepared using the noise models. The proposed method provided effective removal of noise without deteriorating fine lines in the architectural drawings and it showed good performance for various noise types.
KSP Keywords
Artificial Neural Network, CAD drawings, Detail drawings, Hand-drawn, IMAGE FUSION, Noise Removal, Noise characteristics, Noise reduction(NR), Over time, Supervised Learning, floor plans