Deep learning-based 3D image generation using a single 2D projection image

Author(s):  
Yang Lei ◽  
Zhen Tian ◽  
Tonghe Wang ◽  
Justin Roper ◽  
Kristin Higgins ◽  
...  
Author(s):  
Zhaolun Li ◽  
Rushi Lan ◽  
Zhuo Chen ◽  
Xiaonan Luo ◽  
Ji Li ◽  
...  

Author(s):  
Pranoy Ghosh ◽  
Krithika M Pai ◽  
Manohara Pai M M ◽  
Ujjwal Verma ◽  
Frederic Rivet ◽  
...  

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tingting Sun

EditorialIn 2016, the news that Google’s artificial intelligence (AI) robot AlphaGo, based on the principle of deep learning, won the victory over lee Sedol, the former world Go champion and the famous 9th Dan competitor of Korea, caused a sensation in both fields of AI and Go, which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present, deep learning has penetrated into our daily life, such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California, Los Angeles (UCLA) led his team to research deep learning algorithms, which provided new ideas for the exploring of optical computational imaging and sensing technology, and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan, Fellow of the National Academy of Inventors and Chancellor’s Professor of UCLA, to unscramble his latest scientific research results and foresight for the future development of related fields, and to share his journey of pursuing Optics, his indissoluble relationship with Light: Science & Applications (LSA), and his experience in talent cultivation.


Author(s):  
Jin Woo Jung ◽  
Hyun-Wook Kang ◽  
Tae-Yun Kang ◽  
Jeong Hun Park ◽  
Jaesung Park ◽  
...  

2018 ◽  
Author(s):  
Bradley Lowekamp ◽  
David Chen ◽  
Ziv Yaniv ◽  
Terry Yoo

Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a general- ized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative im- plementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).


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