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基于壓縮感知高反光成像技術(shù)研究

范劍英,  馬明陽(yáng),  趙首博

范劍英, 馬明陽(yáng), 趙首博. 基于壓縮感知高反光成像技術(shù)研究[J]. 電子與信息學(xué)報(bào), 2020, 42(4): 1013-1020. doi: 10.11999/JEIT190512
引用本文: 范劍英, 馬明陽(yáng), 趙首博. 基于壓縮感知高反光成像技術(shù)研究[J]. 電子與信息學(xué)報(bào), 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(shù)研究

doi: 10.11999/JEIT190512 cstr: 32379.14.JEIT190512
  • 1.

    哈爾濱理工大學(xué)測(cè)控技術(shù)與通信工程學(xué)院 哈爾濱 150080

  • 2.

    哈爾濱理工大學(xué)測(cè)控技術(shù)與儀器黑龍江省高校重點(diǎn)實(shí)驗(yàn)室 哈爾濱 150080

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61801148, 61803128)，黑龍江省自然科學(xué)基金(QC2016067)
詳細(xì)信息
    作者簡(jiǎn)介:

    范劍英：男，1963年生，教授，碩士生導(dǎo)師，研究方向?yàn)楣怆姍z測(cè)、數(shù)字圖像與重建

    馬明陽(yáng)：男，1993年生，碩士生，研究方向?yàn)閴嚎s感知與數(shù)字信號(hào)處理

    趙首博：男，1985年生，副教授，碩士生導(dǎo)師，研究方向?yàn)榫芄怆姕y(cè)量、計(jì)算視覺(jué)成像

    通訊作者:

    趙首博　shoubozh@126.com

  • 中圖分類(lèi)號(hào): 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(shí)反射的光強(qiáng)容易超出傳感器接收光強(qiáng)的最大量化值，使得采集圖像部分區(qū)域圖像失真，嚴(yán)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(jié)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(cè)量矩陣對(duì)目標(biāo)圖像進(jìn)行線(xiàn)性采樣，將CCD圖像傳感器的單個(gè)光強(qiáng)采樣值與測(cè)量矩陣中的分布數(shù)據(jù)對(duì)應(yīng)結(jié)合，對(duì)整合后的數(shù)據(jù)用算法進(jìn)行恢復(fù)重建實(shí)現(xiàn)被測(cè)目標(biāo)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(guān)評(píng)定標(biāo)準(zhǔn)。實(shí)驗(yàn)表明，該成像方法魯棒性較強(qiáng)、可行性較高，直方圖檢測(cè)飽和像素占比為0%，峰值信噪比為58.37 dB實(shí)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(yīng)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(fù)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(cè)目標(biāo)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(fù)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(dǎo)出CSV
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  • 收稿日期:  2019-07-09
  • 修回日期:  2020-01-17
  • 網(wǎng)絡(luò)出版日期:  2020-02-17
  • 刊出日期:  2020-06-04

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