一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

姓名
郵箱
手機號碼
標題
留言內容
驗證碼

基于壓縮感知高反光成像技術研究

范劍英,  馬明陽,  趙首博

范劍英, 馬明陽, 趙首博. 基于壓縮感知高反光成像技術研究[J]. 電子與信息學報, 2020, 42(4): 1013-1020. doi: 10.11999/JEIT190512
引用本文: 范劍英, 馬明陽, 趙首博. 基于壓縮感知高反光成像技術研究[J]. 電子與信息學報, 2020, 42(4): 1013-1020. doi: 10.11999/JEIT190512
Jianying FAN, Mingyang MA, Shoubo ZHAO. Research on High Reflective Imaging Technology Based on Compressed Sensing[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1013-1020. doi: 10.11999/JEIT190512
Citation: Jianying FAN, Mingyang MA, Shoubo ZHAO. Research on High Reflective Imaging Technology Based on Compressed Sensing[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1013-1020. doi: 10.11999/JEIT190512

基于壓縮感知高反光成像技術研究

doi: 10.11999/JEIT190512
  • 1.

    哈爾濱理工大學測控技術與通信工程學院 哈爾濱 150080

  • 2.

    哈爾濱理工大學測控技術與儀器黑龍江省高校重點實驗室 哈爾濱 150080

基金項目: 國家自然科學基金(61801148, 61803128)，黑龍江省自然科學基金(QC2016067)
詳細信息
    作者簡介:

    范劍英：男，1963年生，教授，碩士生導師，研究方向為光電檢測、數(shù)字圖像與重建

    馬明陽：男，1993年生，碩士生，研究方向為壓縮感知與數(shù)字信號處理

    趙首博：男，1985年生，副教授，碩士生導師，研究方向為精密光電測量、計算視覺成像

    通訊作者:

    趙首博　shoubozh@126.com

  • 中圖分類號: TN911.73; TN957.52

Research on High Reflective Imaging Technology Based on Compressed Sensing

  • 1.

    School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China

  • 2.

    Measurement and Control Technology and Instrument Key Laboratory of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China

Funds: The National Natural Science Foundation of China (61801148, 61803128), The Scientific Research Foundation of Heilongjiang Province (QC2016067)
  • 摘要:

    高反光物體成像時反射的光強容易超出傳感器接收光強的最大量化值，使得采集圖像部分區(qū)域圖像失真，嚴重影響信息傳遞。為了改善高反光成像飽和區(qū)域中數(shù)據(jù)丟失的狀況，該文結合壓縮感知這一新的采樣理論提出基于壓縮感知高反光成像方法，利用特定測量矩陣對目標圖像進行線性采樣，將CCD圖像傳感器的單個光強采樣值與測量矩陣中的分布數(shù)據(jù)對應結合，對整合后的數(shù)據(jù)用算法進行恢復重建實現(xiàn)被測目標在高光環(huán)境中成像。以峰值信噪比和灰度直方圖作為客觀評定標準。實驗表明，該成像方法魯棒性較強、可行性較高，直方圖檢測飽和像素占比為0%，峰值信噪比為58.37 dB實現(xiàn)了在高光環(huán)境下不含飽和光成像，為壓縮感知在成像應用中提供了新的方向。

    Abstract:

    When imaging a highly reflective object, the light intensity reflected easily exceeds the maximum quantized value of the light intensity received by the sensor, which causes image distortion of the captured image in the saturated region of light intensity and seriously affects the quality of information transmission. In order to improve the data loss in the high-reflection imaging saturation region, a compression-sensing of high-reflection imaging method based on the new sampling theory of compressed sensing is proposed. A specific measurement matrix is used to conduct linear sampling of the target image, and the single light intensity sampling value of the CCD image sensor is combined with the distribution data in the measurement matrix, and the integrated data is restored and reconstructed with the algorithm to achieve the imaging of the measured target in the high-light environment. The peak signal to noise ratio and gray histogram are used as objective evaluation criteria. Experiments show that this imaging method is robust and feasible, with the proportion of saturated pixels in histogram detection 0% and the peak signal to noise ratio 58.37 dB, realizing the imaging without saturated light in the high-light environment, providing a new direction for the application of compressed sensing in imaging.

    • HTML全文

  • 圖  1  壓縮感知框架圖

    圖  2  不同環(huán)境下成像狀態(tài)

    圖  3  圖像分塊

    圖  4  CCD所采集含高反光圖像

    圖  5  去除成像中高亮區(qū)域效果

    圖  6  壓縮感知不同恢復算法去除飽和光成像

    圖  7  壓縮感知去飽和光成像光路圖

    圖  8  高亮光環(huán)境下采集的被測目標信息

    圖  9  原始圖像和壓縮感知高反光成像直方圖

    表  1  不同采樣率下兩種恢復算法的MSE值與PSNR值

    CS采樣率OMP算法SAMP算法
    MSEPSNRMSEPSNR
    0.300.015866.14810.016665.9403
    0.350.015066.37390.015466.2512
    0.400.014366.56250.014266.6061
    0.450.013966.71490.013566.8149
    0.500.013666.79280.013366.9078
    下載: 導出CSV
  • 趙首博, 曲興華, 馮維, 等. 成像前光學調制系統(tǒng)的眩光測量[J]. 光電工程, 2016, 43(1): 13–17. doi: 10.3969/j.issn.1003-501X.2016.01.003

    ZHAO Shoubo, QU Xinghua, FENG Wei, et al. A measurement method for glare based on the Optical modulation system before image formation[J]. Opto-Electronic Engineering, 2016, 43(1): 13–17. doi: 10.3969/j.issn.1003-501X.2016.01.003
    范劍英, 劉力源, 趙首博. 電機銅排表面毛刺缺陷檢測技術研究[J]. 儀器儀表學報, 2019, 40(3): 14–22. doi: 10.19650/j.cnki.cjsi.J1804559

    FAN Jianying, LIU Liyuan, and ZHAO Shoubo. Research on detection technology of burr defects in motor copper[J]. Chinese Journal of Scientific Instrument, 2019, 40(3): 14–22. doi: 10.19650/j.cnki.cjsi.J1804559
    ZHAO Shoubo, MA Mingyang, and GUO Cong. Accurate Pixel-to-Pixel alignment method with Six-Axis adjustment for computational photography[J]. IEEE Photonics Journal, 2018, 10(3): 6802416. doi: 10.1109/jphot.2018.2839093
    JIANG Hongzhi, ZHAO Huijie, and LI Xudong. High dynamic range fringe acquisition: A novel 3-D scanning technique for high-reflective surfaces[J]. Optics and Lasers in Engineering, 2012, 50(10): 1484–1493. doi: 10.1016/j.optlaseng.2011.11.021
    ZHAO Shoubo, ZHANG Fumin, QU Xinghua, et al. Removal of parasitic image due to metal specularity based on digital micromirror device camera[J]. Optical Engineering, 2014, 53(6): 063105. doi: 10.1117/1.oe.53.6.063105
    YAN Qingsen, ZHU Yu, and ZHANG Yanning. Robust artifact-free high dynamic range imaging of dynamic scenes[J]. Multimedia Tools and Applications, 2019, 78(9): 11487–11505. doi: 10.1007/s11042-018-6625-x
    KALANTARI N K, SHECHTMAN E, BARNES C, et al. Patch-based high dynamic range video[J]. ACM Transactions on Graphics, 2013, 32(6): No. 202. doi: 10.1145/2508363.2508402
    ZHAO Shoubo, LIU Liyuan, and MA Mingyang. Adaptive high-dynamic range three-dimensional shape measurement using DMD camera[J]. IEEE Access, 2019, 7: 67934–67943. doi: 10.1109/access.2019.2918843
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
    ELDAR Y C, KUTYNIOK G. Compressed Sensing: Theory and Applications[M]. Cambridge: Cambridge University Press, 2012: 1289–1306. doi: 10.1017/CBO9780511794308.
    CANDES E J, ROMBERG J, and TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489–509. doi: 10.1109/TIT.2005.862083
    RISTOBAL-HUERTA A, POOT D H J, VOGEL M W, et al. Compressed sensing 3D-GRASE for faster high-resolution MRI[J]. Magnetic Resonance in Medicine, 2019, 82(3): 984–999. doi: 10.1002/mrm.27789
    ZHU Xiaoxiang and BAMLER R. Superresolving SAR tomography for multidimensional imaging of urban areas: Compressive sensing-based tomoSAR inversion[J]. IEEE Signal Processing Magazine, 2014, 31(4): 51–58. doi: 10.1109/MSP.2014.2312098
    王偉, 胡子英, 龔琳舒. MIMO雷達三維成像自適應Off-grid校正方法[J]. 電子與信息學報, 2019, 41(6): 1294–1301. doi: 10.11999/JEIT180145

    WANG Wei, HU Ziying, and GONG Linshu. Adaptive off-grid calibration method for MIMO radar 3D imaging[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1294–1301. doi: 10.11999/JEIT180145
    金艷, 周磊, 姬紅兵. 基于稀疏時頻分布的跳頻信號參數(shù)估計[J]. 電子與信息學報, 2018, 40(3): 663–669. doi: 10.11999/JEIT170525

    JIN Yan, ZHOU Lei, and JI Hongbing. Parameter estimation of frequency-hopping signals based on sparse time-frequency distribution[J]. Journal of Electronics &Information Technology, 2018, 40(3): 663–669. doi: 10.11999/JEIT170525
    FAZEL F, FAZEL M, and STOJANOVIC M. Random access compressed sensing for energy-efficient underwater sensor networks[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(8): 1660–1670. doi: 10.1109/jsac.2011.110915
    孫玉寶, 李歡, 吳敏, 等. 基于圖稀疏正則化多測量向量模型的高光譜壓縮感知重建[J]. 電子與信息學報, 2014, 36(12): 2942–2948. doi: 10.3724/SP.J.1146.2014.00566

    SUN Yubao, LI Huan, WU Min, et al. Compressed sensing reconstruction of hyperspectral image using the graph sparsity regularized multiple measurement vector model[J]. Journal of Electronics &Information Technology, 2014, 36(12): 2942–2948. doi: 10.3724/SP.J.1146.2014.00566
    LIAO Wenchao, HSIEH J, WANG Chengming, et al. Compressed sensing spectral domain optical coherence tomography with a hardware sparse-sampled camera[J]. Optics Letters, 2019, 44(12): 2955–2958. doi: 10.1364/OL.44.002955
    DUARTE M F, DAVENPORT M A, TAKHAR D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 83–91. doi: 10.1109/msp.2007.914730
    余慧敏, 方廣有. 壓縮感知理論在探地雷達三維成像中的應用[J]. 電子與信息學報, 2010, 32(1): 12–16. doi: 10.3724/SP.J.1146.2009.00040

    YU Huimin and FANG Guangyou. Research on compressive sensing based 3D imaging method applied to ground penetrating radar[J]. Journal of Electronics &Information Technology, 2010, 32(1): 12–16. doi: 10.3724/SP.J.1146.2009.00040
    莊佳衍, 陳錢, 何偉基, 等. 基于壓縮感知的動態(tài)散射成像[J]. 物理學報, 2016, 65(4): 040501. doi: 10.7498/aps.65.040501

    ZHUANG Jiayan, CHEN Qian, HE Weiji, et al. Imaging through dynamic scattering media with compressed sensing[J]. Acta Physica Sinica, 2016, 65(4): 040501. doi: 10.7498/aps.65.040501
    LI Bo, LIU Falin, ZHOU Chongbin, et al. Mixed sparse representation for approximated observation-based compressed sensing radar imaging[J]. Journal of Applied Remote Sensing, 2018, 12(3): 035015. doi: 10.1117/1.JRS.12.035015
    LI Yunhui, WANG Xiaodong, WANG Zhi, et al. Modeling and image motion analysis of parallel complementary compressive sensing imaging system[J]. Optics Communications, 2018, 423: 100–110. doi: 10.1016/j.optcom.2018.04.018
  • 加載中
圖(9) / 表(1)
計量
  • 文章訪問數(shù):  2590
  • HTML全文瀏覽量:  1014
  • PDF下載量:  63
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2019-07-09
  • 修回日期:  2020-01-17
  • 網(wǎng)絡出版日期:  2020-02-17
  • 刊出日期:  2020-06-04

目錄

    /

    返回文章
    返回