.png){kind=icon}
返回
Deep learning-based super-resolution in coherent imaging systems
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-019-40554-1.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-limited coherent imaging systems. The capabilities of this approach are experimentally validated by super-resolving complex-valued images acquired using a lensfree on-chip holographic microscope, the resolution of which was pixel size-limited. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics.
Keyword:
WIDE-FIELD
PIXEL SUPERRESOLUTION
DIGITAL HOLOGRAPHY
PHASE RETRIEVAL
MICROSCOPY
LOCALIZATION
RECOVERY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.3W
被引数:
83.5W
机构
引用论文
Sensitivity of a novel model of mammary cancer stem cell-like cells to TNF-related death pathways
OAAI
OAAI分享收藏Compressive holographic two-dimensional localization with 1/302 subpixel accuracy.png)
OPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imagingLAB ON A CHIP
LAB ON A CHIPIF5.4
PREAI
分享收藏分享收藏分享收藏Deep-STORM: super-resolution single-molecule microscopy by deep learningDeep-STORM: 通过深度学习实现超分辨率单分子显微镜OPTICAOAAI
OPTICAIF8.5
OAAI分享收藏
查看更多内容