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.

Abstract
1. 서 론
2. 연구 방법
    2-1. 지도 학습을 이용한 딥네트워크 기반 도면 노이즈 제거
    2-2. 지도학습을 위한 학습 이미지 데이터 세트 생성
    2-3. CycleGAN을 이용한 학습 이미지 세트 생성
    2-4. 이미지 합성을 이용한 학습 이미지 세트 생성
3. 연구 결과 및 고찰
    3-1. 지도학습을 이용한 딥 네트워크 기반 도면 노이즈 제거
    3-2 노이즈 제거 이미지의 벡터화 성능
4. 논의 및 결론
참고문헌

• 최낙관(울산과학기술원 전자공학과 석사과정) | Choi Nakkwan
• 이용식(한국전자통신연구원) | Lee Yongsik
• 이승재(한국전자통신연구원) | Lee Seungjae
• 양승준(울산과학기술원 전자공학과 교수) | Yang Seungjoon Corresponding author