scholarly journals Holographic Image Reconstruction with Phase Recovery and Autofocusing Using Recurrent Neural Networks

ACS Photonics ◽  
2021 ◽  
Author(s):  
Luzhe Huang ◽  
Tairan Liu ◽  
Xilin Yang ◽  
Yi Luo ◽  
Yair Rivenson ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Recurrent Neural Networks ◽  
Phase Recovery ◽  
Holographic Image

scholarly journals Phase recovery and holographic image reconstruction using deep learning in neural networks

2017 ◽  
Vol 7 (2) ◽  
pp. 17141-17141 ◽  
Author(s):  
Yair Rivenson ◽  
Yibo Zhang ◽  
Harun Günaydın ◽  
Da Teng ◽  
Aydogan Ozcan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Phase Recovery ◽  
Holographic Image

LEARNING ITERATIVE IMAGE RECONSTRUCTION IN THE NEURAL ABSTRACTION PYRAMID

2001 ◽  
Vol 01 (04) ◽  
pp. 427-438 ◽  
Author(s):  
SVEN BEHNKE
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Recurrent Neural Networks ◽  
Multiple Scales ◽  
Noise Removal ◽  
Hierarchical Architecture ◽  
Context Information ◽  
Analysis And Synthesis ◽  
Local Connection ◽  
Degraded Images

Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. We propose to use recurrent neural networks for both analysis and synthesis. The networks have a hierarchical architecture that represents images in multiple scales with different degrees of abstraction. The mapping between these representations is mediated by a local connection structure. We supply the networks with degraded images and train them to reconstruct the originals iteratively. This iterative reconstruction makes it possible to use partial results as context information to resolve ambiguities. We demonstrate the power of the approach using three examples: superresolution, fill-in of occluded parts, and noise removal/contrast enhancement. We also reconstruct images from sequences of degraded images.

scholarly journals Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

2019 ◽  
Vol 38 (1) ◽  
pp. 280-290 ◽  
Author(s):  
Chen Qin ◽  
Jo Schlemper ◽  
Jose Caballero ◽  
Anthony N. Price ◽  
Joseph V. Hajnal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Recurrent Neural Networks ◽  
Mr Image ◽  
Dynamic Mr ◽  
Mr Image Reconstruction

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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