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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 2,163 - 2,167 (5page)
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초록· 키워드
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Low-dose computed tomography (LDCT) has become an essential tool for reducing radiation exposure while still providing vital anatomical information necessary for diagnosing various pathologies. However, the inevitable loss of information, including issues like noise, streak artifacts, and blurred details, can adversely affect clinical diagnostics. To enhance LDCT imaging, there is notable potential in effectively minimizing image noise while retaining crucial anatomical features. This paper introduces an innovative dual-branch network architecture for LDCT denoising that combines edge-guided compound loss with multiple optimal image-based techniques. We evaluate the denoising performance by comparing various the latest state-of-the-art (SOTA) algorithms and demonstrating that our proposed model surpasses the SOTA algorithms. The proposed method shows that both the objective and perceptual quality of LDCT images are improved.
목차
Abstract
Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 구현 및 평가
Ⅳ. 결론 및 향후 연구 방향
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