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Real-time biodetection using a smartphone-based dual-color surface plasmon resonance sensor.
期刊论文
Journal of biomedical optics, 2018, 卷号: 23, 页码: 1-6
作者:
Liu, Qiang
;
Yuan, Huizhen
;
Liu, Yun
;
Wang, Jiabin
;
Jing, Zhenguo
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/02
optical fiber sensor
surface plasmon resonance
biological sensing and sensors
visibility and imaging
Fast polarimetric dehazing method for visibility enhancement in HSI colour space
期刊论文
JOURNAL OF OPTICS, 2017, 卷号: 19, 期号: 9
作者:
Zhang, Wenfei
;
Liang, Jian
;
Ren, Liyong
;
Ju, Haijuan
;
Bai, Zhaofeng
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2017/09/19
Polarimetric Imaging
Visibility And Imaging
Image Enhancement
Imaging Through Turbid Media
Fast polarimetric dehazing method for visibility enhancement in HSI colour space
期刊论文
JOURNAL OF OPTICS, 2017, 卷号: 19
作者:
Zhang, Wenfei
;
Liang, Jian
;
Ren, Liyong
;
Ju, Haijuan
;
Bai, Zhaofeng
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/11/26
visibility and imaging
image enhancement
imaging through turbid media
polarimetric imaging
Study of visibility enhancement of hazy images based on dark channel prior in polarimetric imaging
期刊论文
OPTIK, 2017, 卷号: 130, 页码: 123-130
作者:
Zhang, Wenfei
;
Liang, Jian
;
Ju, Haijuan
;
Ren, Liyong
;
Qu, Enshi
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/11/26
Scattering
Visibility and imaging
Image enhancement
Polarimetric imaging
Study of visibility enhancement of hazy images based on dark channel prior in polarimetric imaging
期刊论文
optik, 2017, 卷号: 130, 页码: 123-130
作者:
Zhang, Wenfei
;
Liang, Jian
;
Ju, Haijuan
;
Ren, Liyong
;
Qu, Enshi
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  |  
浏览/下载:58/0
  |  
提交时间:2016/12/21
Image enhancement
Polarimetric imaging
Scattering
Visibility and imaging
A robust haze-removal scheme in polarimetric dehazing imaging based on automatic identification of sky region
期刊论文
optics and laser technology, 2016, 卷号: 86, 页码: 145-151
作者:
Zhang, Wenfei
;
Liang, Jian
;
Ju, Haijuan
;
Ren, Liyong
;
Qu, Enshi
收藏
  |  
浏览/下载:29/0
  |  
提交时间:2016/10/14
Imaging through turbid media
Image enhancement
Polarimetric imaging
Visibility and imaging
A robust haze-removal scheme in polarimetric dehazing imaging based on automatic identification of sky region
期刊论文
OPTICS AND LASER TECHNOLOGY, 2016, 卷号: 86, 期号: [db:dc_citation_issue], 页码: 145-151
作者:
Zhang, Wenfei
;
Liang, Jian
;
Ju, Haijuan
;
Ren, Liyong
;
Qu, Enshi
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/12/02
Visibility and imaging
Image enhancement
Imaging through turbid media
Polarimetric imaging
Resolution and visibility of two-color pseudothermal lensless ghost imaging
期刊论文
optik, 2014, 卷号: 125, 期号: 16, 页码: 4452-4455
作者:
Xue, Yu-Lang
;
Wan, Ren-Gang
;
Feng, Fei
;
Zhang, Tong-Yi
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  |  
浏览/下载:23/0
  |  
提交时间:2015/03/13
Two-color lensiess ghost imaging
Pseudothermal light source
Resolution and visibility
Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE)
会议论文
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Sun J.
;
Ma D.
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  |  
浏览/下载:21/0
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提交时间:2013/03/25
The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task
for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type
the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP
but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly
we use frame grabber to capture the 4-bar target image data. Then according to image gray scale
we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets
along with known target visibility
are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm
demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.
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