인문학
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자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.6
- 수록면
- 733 - 740 (8page)
- DOI
- 10.9717/kmms.2025.28.6.734
이용수
초록· 키워드
Super-resolution(SR) is the image processing that reconstructs high-resolution images from low-resolution inputs. Representative models such as ASCNN distinguish between high and low frequency regions and apply a Low Parameter Convolution(LPC) with reduced channel dimensions to the low frequency regions. Although various other lightweight models have also been proposed, balancing computational cost and reconstruction quality remains a significant challenge. In this paper, we propose DW-ASCNN, a lightweight variant of the original ASCNN, in which the LPC layers are replaced with depthwise and pointwise convolutions. The proposed structure identifies distinct convolutional paths by leveraging the frequency characteristics of each region in ASCNN, and achieves parameter and computational efficiency through structural simplification of these separated paths. Experimental results show that DW-ASCNN reduces the number of parameters by approximately 24% and lowers FLOPs by up to 7.93% compared to the original ASCNN, while keeping the PSNR degradation within an average of 0.02 dB. These results show that the proposed model is well-suited for deployment in resource-con-strained environments.
#Area-Specific Convolutions
#Depthwise Convolution
#Pointwise Convolution
#Computation Reduction
#Convolutional Neural Network
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목차
- ABSTRACT
- 1. 서론
- 2. 이론
- 3. 제안한 방법
- 4. 실험결과 및 고찰
- 5. 결론
- REFERENCE
참고문헌
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