论文标题
可区分显微镜设计所有光相检索显微镜
Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope
论文作者
论文摘要
自16世纪后期以来，科学家一直为各种应用创新并开发了新的显微镜类型。从头开始建立新的架构需要大量的科学专业知识和创造力，通常是数年甚至几十年。在这项研究中，我们提出了一种称为“可区分显微镜”的替代方法，该方法引入了用于光学显微镜的自上而下的设计范式。使用全光相检索为说明性示例，我们通过$ \partialμ$证明了数据驱动的显微镜设计的有效性。此外，我们与竞争方法进行了全面的比较，展示了我们在包括生物样品在内的多个数据集中学习设计的一致性优势。为了证实我们的想法，我们在实验中验证了一种学识渊博的设计的功能，提供了概念证明。所提出的可区分显微镜框架为设计新的光学系统设计的创作过程提供了创作过程，并可能导致非常规但更好的光学设计。
Since the late 16th century, scientists have continuously innovated and developed new microscope types for various applications. Creating a new architecture from the ground up requires substantial scientific expertise and creativity, often spanning years or even decades. In this study, we propose an alternative approach called "Differentiable Microscopy," which introduces a top-down design paradigm for optical microscopes. Using all-optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscopy design through $\partialμ$. Furthermore, we conduct comprehensive comparisons with competing methods, showcasing the consistent superiority of our learned designs across multiple datasets, including biological samples. To substantiate our ideas, we experimentally validate the functionality of one of the learned designs, providing a proof of concept. The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs.
