A deep learning approach for noise reduction of off-axis computer generated holograms

2021 ◽  
Author(s):  
XuSheng Zhuang ◽  
Aimin Yan
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Learning Approach ◽  
Computer Generated Holograms

scholarly journals Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography

2019 ◽  
Vol 27 (4) ◽  
pp. 4927 ◽  
Author(s):  
Gunho Choi ◽  
DongHun Ryu ◽  
YoungJu Jo ◽  
Young Seo Kim ◽  
Weisun Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Optical Diffraction ◽  
Learning Approach ◽  
Diffraction Tomography ◽  
Coherent Noise ◽  
Optical Diffraction Tomography

A Deep Learning Approach for Digital Hologram Speckle Noise Reduction

2020 ◽  
Author(s):  
Wen-Jing Zhou ◽  
Shili Liu ◽  
Hongbo Zhang ◽  
Yingjie Yu ◽  
Ting-Chung Poon
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Speckle Noise ◽  
Learning Approach ◽  
Digital Hologram ◽  
Speckle Noise Reduction

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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