IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Deep Learning-based Deraining: Performance Comparison and Trends

딥러닝 기반 Deraining 기법 비교 및 연구 동향

  • Received : 2021.08.13
  • Accepted : 2021.10.07
  • Published : 2021.10.31
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Abstract

Deraining is one of the image restoration tasks and should consider a tradeoff between local details and broad contextual information while recovering images. Current studies adopt an attention mechanism which has been actively researched in natural language processing to deal with both global and local features. This paper classifies existing deraining methods and provides comparative analysis and performance comparison by using several datasets in terms of generalization.

Keywords

Acknowledgement

본 연구는 문화체육관광부의 자율주행 차량 기반 다수 시나리오 실시간 인터랙티브 콘텐츠 및 플랫폼 기술 개발사업의 연구비지원(No.R2020040058) 및 행정안전부/국토교통과학기술진흥원의 지원으로 수행되었음 (No.21PQWO-B153358-03).

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