scholarly journals Temporal Shape Super-Resolution by Intra-frame Motion Encoding Using High-fps Structured Light

2017 ◽  
Author(s):  
Yuki Shiba ◽  
Satoshi Ono ◽  
Ryo Furukawa ◽  
Shinsaku Hiura ◽  
Hiroshi Kawasaki
Keyword(s):  
Structured Light ◽  
Super Resolution ◽  
Temporal Shape ◽  
Frame Motion

scholarly journals Generation and Detection of Structured Light: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Jian Wang ◽  
Yize Liang
Keyword(s):  
Quantum Information Processing ◽  
Structured Light ◽  
Super Resolution ◽  
Research Progress ◽  
Free Space Optical ◽  
The Past ◽  
Integrated Devices ◽  
Different Types ◽  
Resolution Imaging ◽  
Light Beams

Structured light beams have rapidly advanced over the past few years, from specific spatial-transverse/longitudinal structure to tailored spatiotemporal structure. Such beams with diverse spatial structures or spatiotemporal structures have brought various breakthroughs to many fields, including optical communications, optical sensing, micromanipulation, quantum information processing, and super-resolution imaging. Thus, plenty of methods have been proposed, and lots of devices have been manufactured to generate structured light beams by tailoring the structures of beams in the space domain and the space–time domain. In this paper, we firstly give a brief introduction of different types of structured light. Then, we review the recent research progress in the generation and detection of structured light on different platforms, such as free space, optical fiber, and integrated devices. Finally, challenges and perspectives are also discussed.

Structured Light Optical Super-Resolution: Encoding for Limited Optical Bandwidth

2012 ◽  
Author(s):  
Marc P. Christensen ◽  
Prasanna Rangarajan ◽  
Indranil Sinharoy ◽  
Predrag Milojkovic
Keyword(s):  
Structured Light ◽  
Super Resolution ◽  
Optical Bandwidth

scholarly journals Super-resolution imaging using proximity projection grating and structured light illumination

2013 ◽  
Vol 21 (13) ◽  
pp. 15155 ◽  
Author(s):  
Chung W. See ◽  
Feng Hu ◽  
Chin-Jung Chuang ◽  
Michael G. Somekh
Keyword(s):  
Structured Light ◽  
Super Resolution ◽  
Light Illumination ◽  
Structured Light Illumination ◽  
Resolution Imaging

scholarly journals Shedding Structured Light on Molecular Immunity: The Past, Present and Future of Immune Cell Super Resolution Microscopy

2021 ◽  
Vol 12 ◽  
Author(s):  
Timothy M. Johanson ◽  
Christine R. Keenan ◽  
Rhys S. Allan
Keyword(s):  
Immune System ◽  
Immune Cell ◽  
Structured Light ◽  
Super Resolution ◽  
The Past ◽  
Resolution Imaging ◽  
Super Resolution Microscopy ◽  
The Way

In the two decades since the invention of laser-based super resolution microscopy this family of technologies has revolutionised the way life is viewed and understood. Its unparalleled resolution, speed, and accessibility makes super resolution imaging particularly useful in examining the highly complex and dynamic immune system. Here we introduce the super resolution technologies and studies that have already fundamentally changed our understanding of a number of central immunological processes and highlight other immunological puzzles only addressable in super resolution.

scholarly journals Video Face Super-Resolution with Motion-Adaptive Feedback Cell

2020 ◽  
Vol 34 (07) ◽  
pp. 12468-12475
Author(s):  
Jingwei Xin ◽  
Nannan Wang ◽  
Jie Li ◽  
Xinbo Gao ◽  
Zhifeng Li
Keyword(s):  
Motion Estimation ◽  
State Of The Art ◽  
Super Resolution ◽  
Complex Motion ◽  
Deep Convolutional Neural Networks ◽  
Adaptive Feedback ◽  
Current State ◽  
Model Complex ◽  
Frame Motion ◽  
Inter Frame

Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames. However, duo to the complex temporal dependency between frames, with the number of LR input frames increase, the performance of the reconstructed HR frames become worse. The reason is in that these methods lack the ability to model complex temporal dependencies and hard to give an accurate motion estimation and compensation for VSR process. Which makes the performance degrade drastically when the motion in frames is complex. In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way. Our approach efficiently utilizes the information of the inter-frame motion, the dependence of the network on motion estimation and compensation method can be avoid. In addition, benefiting from the excellent nature of MAFC, the network can achieve better performance in the case of extremely complex motion scenarios. Extensive evaluations and comparisons validate the strengths of our approach, and the experimental results demonstrated that the proposed framework is outperform the state-of-the-art methods.

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