User profiles for Harun Günaydin
Harun GunaydinFacebook Verified email at fb.com Cited by 3347 |
Deep learning microscopy
We demonstrate that a deep neural network can significantly improve optical microscopy,
enhancing its spatial resolution over a large field of view and depth of field. After its training, …
enhancing its spatial resolution over a large field of view and depth of field. After its training, …
Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery
Holography encodes the three-dimensional (3D) information of a sample in the form of an
intensity-only recording. However, to decode the original sample image from its hologram(s), …
intensity-only recording. However, to decode the original sample image from its hologram(s), …
Deep learning enhanced mobile-phone microscopy
Mobile phones have facilitated the creation of field-portable, cost-effective imaging and
sensing technologies that approach laboratory-grade instrument performance. However, the …
sensing technologies that approach laboratory-grade instrument performance. However, the …
[HTML][HTML] Phase recovery and holographic image reconstruction using deep learning in neural networks
Phase recovery from intensity-only measurements forms the heart of coherent imaging
techniques and holography. In this study, we demonstrate that a neural network can learn to …
techniques and holography. In this study, we demonstrate that a neural network can learn to …
Deep learning enables cross-modality super-resolution in fluorescence microscopy
We present deep-learning-enabled super-resolution across different fluorescence
microscopy modalities. This data-driven approach does not require numerical modeling of the …
microscopy modalities. This data-driven approach does not require numerical modeling of the …
Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
The histological analysis of tissue samples, widely used for disease diagnosis, involves
lengthy and laborious tissue preparation. Here, we show that a convolutional neural network …
lengthy and laborious tissue preparation. Here, we show that a convolutional neural network …
Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
Histological analysis of tissue samples is one of the most widely used methods for disease
diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious …
diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious …
Deep learning achieves super-resolution in fluorescence microscopy
We present a deep learning-based method for achieving super-resolution in fluorescence
microscopy. This data-driven approach does not require any numerical models of the imaging …
microscopy. This data-driven approach does not require any numerical models of the imaging …
Deep learning microscopy: enhancing resolution, field-of-view and depth-of-field of optical microscopy images using neural networks
… Yair Rivenson1,2,3,§ , Zoltán Göröcs,1,2,3,† Harun Günaydın,1,† Yibo Zhang,1,2,3
Hongda Wang,1,2,3 Aydogan … Günaydin, Y. Zhang, H. Wang, and A. … Günaydin, D …
Hongda Wang,1,2,3 Aydogan … Günaydin, Y. Zhang, H. Wang, and A. … Günaydin, D …
Cross-modality deep learning achieves super-resolution in fluorescence microscopy
…, Y Jin, Z Wei, R Gao, H Günaydin… - 2019 Conference on …, 2019 - ieeexplore.ieee.org
Using cross-modality deep learning, we achieved super-resolution in fluorescence microscopy
and established image transformations from a lower resolution microscopy modality to a …
and established image transformations from a lower resolution microscopy modality to a …