Holographic Microscopy with Python and HoloPy
A holographic microscope captures interference patterns, or holograms, that encode three-dimensional (3D) information about the object being viewed. Computation is essential to extracting that 3D information. By wrapping low-level scattering codes and taking advantage of Python's data analysis ecosystem, HoloPy makes it easy for experimentalists to use modern, sophisticated inference methods to analyze holograms. The resulting data can be used to understand how small particles or microorganisms move and interact. The project illustrates how computational tools can enable experimental methods and new experiments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Automated Pollen Recognition in Optical and Holographic Microscopy Images
This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on veterinary cytology use…
Image ClassificationObject DetectionQuantitative assessment of changes in cellular morphology at photodynamic treatment in vitro by means of digital holographic microscopy
Changes in morphological characteristics of cells from two cultured cancer cell lines, HeLa and A549, induced by photodynamic treatment with Radachlorin photosensitizer have been monitored using digital holographic micro…
Deep learning-based holographic polarization microscopy
Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected fr…
Deep LearningDiagnosticMedical DiagnosisCross-modality deep learning brings bright-field microscopy contrast to holography
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of bright-field …
Deep LearningeFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
The application of deep learning techniques has greatly enhanced holographic imaging capabilities, leading to improved phase recovery and image reconstruction. Here, we introduce a deep neural network termed enhanced Fou…
Image ReconstructionSuper-Resolution