Large depth of field high-resolution 3D information fusion for optical-resolution photoacoustic microscopy using wavelet transform

2021 ◽  
Author(s):  
Xiongjun Cao ◽  
Zhihui Li ◽  
Xianlin Song
Keyword(s):  
Wavelet Transform ◽  
High Resolution ◽  
Information Fusion ◽  
Optical Resolution ◽  
Depth Of Field ◽  
Photoacoustic Microscopy ◽  
Large Depth ◽  
3D Information

Large-depth-of-field optical-resolution colorectal photoacoustic endoscope

2019 ◽  
Vol 114 (16) ◽  
pp. 163703 ◽  
Author(s):  
Xiaowan Li ◽  
Kedi Xiong ◽  
Sihua Yang
Keyword(s):  
Optical Resolution ◽  
Depth Of Field ◽  
Large Depth

scholarly journals Visually Guided Microassembly with Active Zooming

2006 ◽  
Vol 18 (6) ◽  
pp. 787-794
Author(s):  
Xiaodong Tao ◽  
◽  
Hyungsuck Cho ◽  
Youngjun Cho ◽  
Keyword(s):  
High Resolution ◽  
Field Of View ◽  
Depth Of Field ◽  
Wide Field ◽  
Small Field ◽  
Depth Of Focus ◽  
Large Depth ◽  
Small Depth ◽  
Wide Field Of View ◽  
Small Field Of View

Vision techniques used in automatic microassembly are limited by inherent problems such as small depth of focus and small field of view. Microassembly must, however, initially detect microparts in a wide field of view and large depth of field while maintaining high resolution. We propose microassembly using active zooming that can overcome these limitations. For a small field of view, active zooming prevents the target from getting out of the field of view during microassembly. For a small depth of focus, our proposal is based on focus measure to maintain clear target image in the field of view during microassembly. Two-step assembly thus ensures zoom microscopy maintaining a wide field of view and large depth of field initially and high resolution at the end. Peg-in-hole assembly experiments confirmed the feasibility of our proposal.

scholarly journals 3D-information fusion from very high resolution satellite sensors

2015 ◽  
Vol XL-7/W3 ◽  
pp. 651-656 ◽  
Author(s):  
T. Krauss ◽  
P. d'Angelo ◽  
G. Kuschk ◽  
J. Tian ◽  
T. Partovi
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Information Fusion ◽  
Renewable Energy Sources ◽  
Open Pit ◽  
Open Pit Mining ◽  
Surface Model ◽  
Digital Terrain ◽  
3D Information ◽  
Very High

In this paper we show the pre-processing and potential for environmental applications of very high resolution (VHR) satellite stereo imagery like these from WorldView-2 or Pl´eiades with ground sampling distances (GSD) of half a metre to a metre. To process such data first a dense digital surface model (DSM) has to be generated. Afterwards from this a digital terrain model (DTM) representing the ground and a so called normalized digital elevation model (nDEM) representing off-ground objects are derived. Combining these elevation based data with a spectral classification allows detection and extraction of objects from the satellite scenes. Beside the object extraction also the DSM and DTM can directly be used for simulation and monitoring of environmental issues. Examples are the simulation of floodings, building-volume and people estimation, simulation of noise from roads, wave-propagation for cellphones, wind and light for estimating renewable energy sources, 3D change detection, earthquake preparedness and crisis relief, urban development and sprawl of informal settlements and much more. Also outside of urban areas volume information brings literally a new dimension to earth oberservation tasks like the volume estimations of forests and illegal logging, volume of (illegal) open pit mining activities, estimation of flooding or tsunami risks, dike planning, etc. In this paper we present the preprocessing from the original level-1 satellite data to digital surface models (DSMs), corresponding VHR ortho images and derived digital terrain models (DTMs). From these components we present how a monitoring and decision fusion based 3D change detection can be realized by using different acquisitions. The results are analyzed and assessed to derive quality parameters for the presented method. Finally the usability of 3D information fusion from VHR satellite imagery is discussed and evaluated.

High-resolution 3D fusion method for optical-resolution photoacoustic microscopy using deep learning

2021 ◽  
Author(s):  
Sihang Li ◽  
Zhuangzhuang Wang ◽  
Bofang Chen ◽  
Chenghao Gu ◽  
Ziming Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Optical Resolution ◽  
Fusion Method ◽  
Photoacoustic Microscopy
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