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.
초록·키워드
Context . Post-facto image restoration techniques are essential for improving the quality of ground-based astronomical observations, which are affected by atmospheric turbulence. Multi-object, multi-frame blind deconvolution (MOMFBD) methods are widely used in solar physics to achieve diffraction-limited imaging. Aims . We present torchmfbd , a new open-source code for MOMFBD that leverages the PyTorch library to provide a flexible, GPU-accelerated framework for image restoration. The code is designed to handle spatially variant point spread functions (PSFs) and includes advanced regularization techniques. Methods . The code implements the MOMFBD method using a maximum a posteriori estimation framework. It supports both wavefront-based and data-driven PSF parameterizations, including a novel experimental approach using nonnegative matrix factorization. Regularization techniques, such as smoothness and sparsity constraints, can be incorporated to stabilize the solution. The code also supports dividing large fields of view into patches and includes tools for apodization and destretching. The code architecture is designed to become a flexible platform over which new reconstruction and regularization methods can also be straightforwardly implemented. Results . We demonstrate the capabilities of torchmfbd on real solar observations, showing its ability to produce high-quality reconstructions efficiently. The GPU acceleration significantly reduces computation time, making the code suitable for large datasets.
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.
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