Midinfrared LEDs versus thermal emitters in IR dynamic scene simulation devices

Scene simulation technique has been widely used on the field of weapon developing. However, the problems such as technique intricacy and the difficulty of cooperating with work prevent it further developing. So the scene simulation approach to the complex system is put forward based on UML. It can be used to manage effectively the relation of scene simulation models with UML, the organization of models, and the maintenance or the modification of the simulation, to implement till the optimized project. Practical application showed that the approach is available for visualization of numerical calculation, simulation model, simulation interaction, maintenance and modification of scene simulation. It can improve actual operation efficiency considerable.

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The optical system for infrared dynamic scene simulation

10.1117/12.572701 ◽

2005 ◽

Author(s):

Yawei Zheng ◽

Jiaobo Gao ◽

Jun Wang ◽

Huiling Chen

Keyword(s):

Optical System ◽

Dynamic Scene ◽

Scene Simulation

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Multispectral dynamic scene simulation system for flying targets

10.1117/12.834022 ◽

2009 ◽

Author(s):

Zhangye Wang ◽

Zuosheng Wang ◽

Cheng Ye ◽

Junwen Liang

Keyword(s):

Simulation System ◽

Dynamic Scene ◽

Scene Simulation

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A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

Applied Sciences ◽

10.3390/app11052174 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2174

Author(s):

Xiaoguang Li ◽

Feifan Yang ◽

Jianglu Huang ◽

Li Zhuo

Keyword(s):

Local Features ◽

Attention Mechanism ◽

Experimental Results ◽

Dynamic Scene ◽

Feature Maps ◽

Training Scheme ◽

Real Scene ◽

Global And Local

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.

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