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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
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

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。

  • 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
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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

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