Deep learning-based optical field screening for robust optical diffraction tomography
Abstract In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rul...
Guardado en:
Autores principales: | DongHun Ryu, YoungJu Jo, Jihyeong Yoo, Taean Chang, Daewoong Ahn, Young Seo Kim, Geon Kim, Hyun-Seok Min, YongKeun Park |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/9eecb8381ec241acaec2addcce5caa2a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Detection of intracellular monosodium urate crystals in gout synovial fluid using optical diffraction tomography
por: Sangwoo Park, et al.
Publicado: (2021) -
Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
por: Jonghee Yoon, et al.
Publicado: (2017) -
Tomographic active optical trapping of arbitrarily shaped objects by exploiting 3D refractive index maps
por: Kyoohyun Kim, et al.
Publicado: (2017) -
Characterizing right-angled vessel in macular telangiectasia type 2 with structural optical coherence tomography
por: Yoo-Ri Chung, et al.
Publicado: (2021) -
Chip-scale atomic diffractive optical elements
por: Liron Stern, et al.
Publicado: (2019)