인문학
사회과학
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.
피인용 0
초록·키워드
This paper introduces MobileDMD-Net, a lightweight deep neural network designed for real-time single-image motion deblurring in dynamic scenes. Unlike conventional approaches that rely on computationally intensive architectures or handcrafted priors, MobileDMD-Net employs a compact, encoder-decoder design optimized for efficiency without com- promising image restoration quality. The model integrates a blur-aware attention mechanism and frequency-guided feature fusion to effectively disentangle motion blur across varying spatial scales. Extensive experiments conducted on the Go- Pro and Köhler datasets demonstrate that MobileDMD-Net achieves state-of-the-art performance, with PSNR and SSIM improvements of up to 3.17 dB and 0.034, respectively, over strong baselines. Notably, the model requires only 4.10
GFLOPs and achieves an inference time of 0.014 seconds, enabling deployment on real-time systems. These characteristics make it well-suited for latency-sensitive applications such as onboard image enhancement for unmanned aerial vehicles (UAVs) and edge computing environments. The proposed method represents a compelling trade-off between accuracy and computational cost, advancing the practical feasibility of high-quality motion deblurring in real-world scenarios.
GFLOPs and achieves an inference time of 0.014 seconds, enabling deployment on real-time systems. These characteristics make it well-suited for latency-sensitive applications such as onboard image enhancement for unmanned aerial vehicles (UAVs) and edge computing environments. The proposed method represents a compelling trade-off between accuracy and computational cost, advancing the practical feasibility of high-quality motion deblurring in real-world scenarios.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-26-02-096897062
