PCA-based denoising method for division of focal plane polarimeters

被引：52
|
作者：
Zhang, Junchao [1 ,2 ,3 ]
Luo, Haibo [1 ,3 ,4 ]
Liang, Rongguang [5 ]
Zhou, Wei [6 ]
Hui, Bin [1 ,3 ,4 ]
Chang, Zheng [1 ,3 ,4 ]
机构：
[1] Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Chinese Acad Sci, Key Lab Opt Elect Informat Proc, Shenyang 110016, Peoples R China
[4] Key Lab Image Understanding & Comp Vis, Shenyang 110016, Peoples R China
[5] Univ Arizona, Ctr Opt Sci, Tucson, AZ 85721 USA
[6] AVIC Jiangxi HONGDU Aviat Ind Grp LTD, Nanchang 220024, Jiangxi, Peoples R China
来源：
OPTICS EXPRESS | 2017年 / 25卷 / 03期
关键词：
IMAGE INTERPOLATION;
D O I：
10.1364/OE.25.002391
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Division of focal plane (DoFP) polarimeters are composed of interlaced linear polarizers overlaid upon a focal plane array sensor. The interpolation is essential to reconstruct polarization information. However, current interpolation methods are based on the unrealistic assumption of noise-free images. Thus, it is advantageous to carry out denoising before interpolation. In this paper, we propose a principle component analysis (PCA) based denoising method, which works directly on DoFP images. Both simulated and real DoFP images are used to evaluate the denoising performance. Experimental results show that the proposed method can effectively suppress noise while preserving edges. (C) 2017 Optical Society of America
引用
收藏
页码：2391 / 2400
页数：10
相关论文
共 50 条
  • [41] GP-grid image interpolation and denoising for division of focal plane sensors
    Gilboa, Elad
    Cunningham, John P.
    Nehorai, Arye
    Gruev, Viktor
    POLARIZATION: MEASUREMENT, ANALYSIS, AND REMOTE SENSING XI, 2014, 9099
  • [42] Image interpolation and denoising for division of focal plane sensors using Gaussian processes
    Gilboa, Elad
    Cunningham, John P.
    Nehorai, Arye
    Gruev, Viktor
    OPTICS EXPRESS, 2014, 22 (12): : 15277 - 15291
  • [43] Division-of-Focal-Plane Polarization Image Denoising Algorithm Based on Improved Principal Component Analysis
    Yin Jiaqi
    Wang Shiyong
    Li Fanming
    ACTA OPTICA SINICA, 2021, 41 (07)
  • [44] Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction?
    Duan, Yao
    Yang, Chuanchuan
    Chen, Hao
    Yan, Weizhen
    Li, Hongbin
    OPTICS COMMUNICATIONS, 2021, 482
  • [45] EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking
    Peng, Kang
    Guo, Hongyang
    Shang, Xueyi
    SENSORS, 2021, 21 (16)
  • [46] PCA-based magnification method for revealing small signals in video
    Wu, Xiu
    Yang, Xuezhi
    Jin, Jing
    Yang, Zhao
    SIGNAL IMAGE AND VIDEO PROCESSING, 2018, 12 (07) : 1293 - 1299
  • [47] A PCA-Based Approach to the Representation and Recognition of MR Brain Midsagittal Plane Images
    Zhang, Ye
    Hu, Qingmao
    2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8, 2008, : 3916 - 3919
  • [48] Blind Image Restoration Method by PCA-based Subspace Generation
    Sumali, Brian
    Hamada, Nozomu
    Mitsukura, Yasue
    2015 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS), 2015, : 204 - 209
  • [49] A PCA-Based Automated Method for Determination of Human Body Orientation
    Wu, Weihe
    Hao, Aimin
    Wang, Wentao
    Wang, Yiqiang
    INTELLIGENCE COMPUTATION AND EVOLUTIONARY COMPUTATION, 2013, 180 : 327 - 337
  • [50] PCA-based magnification method for revealing small signals in video
    Xiu Wu
    Xuezhi Yang
    Jing Jin
    Zhao Yang
    Signal, Image and Video Processing, 2018, 12 : 1293 - 1299
12345