Deep neural network based single pixel prediction for unified video coding

2018 ◽  
Vol 272 ◽  
pp. 558-570 ◽  
Author(s):  
Honggui Li ◽  
Maria Trocan
2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Fei Wang ◽  
Chenglong Wang ◽  
Mingliang Chen ◽  
Wenlin Gong ◽  
Yu Zhang ◽  
...  

AbstractGhost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.


Author(s):  
Jongho Kim ◽  
Dae Yeol Lee ◽  
Seyoon Jeong ◽  
Seunghyun Cho

2021 ◽  
Author(s):  
Santosh Kumar ◽  
Ting Bu ◽  
He Zhang ◽  
Irwin Huang ◽  
Yu-Ping Huang

Author(s):  
Bouthaina Abdallah ◽  
Fatma Belghith ◽  
Mohamed Ali Ben Ayed ◽  
Nouri Masmoudi

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