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基于多通道多尺度卷積神經(jīng)網(wǎng)絡(luò)的單幅圖像去雨方法

柳長源 王琪 畢曉君

柳長源, 王琪, 畢曉君. 基于多通道多尺度卷積神經(jīng)網(wǎng)絡(luò)的單幅圖像去雨方法[J]. 電子與信息學(xué)報, 2020, 42(9): 2285-2292. doi: 10.11999/JEIT190755
引用本文: 柳長源, 王琪, 畢曉君. 基于多通道多尺度卷積神經(jīng)網(wǎng)絡(luò)的單幅圖像去雨方法[J]. 電子與信息學(xué)報, 2020, 42(9): 2285-2292. doi: 10.11999/JEIT190755
Changyuan LIU, Qi WANG, Xiaojun BI. Research on Rain Removal Method for Single Image Based on Multi-channel and Multi-scale CNN[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2285-2292. doi: 10.11999/JEIT190755
Citation: Changyuan LIU, Qi WANG, Xiaojun BI. Research on Rain Removal Method for Single Image Based on Multi-channel and Multi-scale CNN[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2285-2292. doi: 10.11999/JEIT190755

基于多通道多尺度卷積神經(jīng)網(wǎng)絡(luò)的單幅圖像去雨方法

doi: 10.11999/JEIT190755
基金項目: 國家自然科學(xué)基金(51779050)
詳細信息
    作者簡介:

    柳長源:男,1970年生,副教授,工學(xué)博士,碩士生導(dǎo)師,研究方向為模式識別與圖像處理,機器學(xué)習(xí)與優(yōu)化方法

    王琪:女,1996年生,碩士生,研究方向為模式識別與圖像處理

    畢曉君:女,1964年生,教授,博士生導(dǎo)師,研究方向為數(shù)字圖像處理、信息智能處理技術(shù)及通信信息處理技術(shù)

    通訊作者:

    王琪 1208401521@qq.com

  • 中圖分類號: TN911.73; TP391.4

Research on Rain Removal Method for Single Image Based on Multi-channel and Multi-scale CNN

Funds: The National Natural Science Foundation of China(51779050)
  • 摘要: 雨天等惡劣天氣會嚴重影響到圖像成像質(zhì)量,從而影響到視覺處理算法的性能。為了改善雨天圖像的成像質(zhì)量,該文提出一種基于多通道多尺度卷積神經(jīng)網(wǎng)絡(luò)的去雨算法,建立了多通道多尺度卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)來提取雨線特征。首先利用小波閾值引導(dǎo)的雙邊濾波將有雨圖像進行分解,得到高頻雨線圖像和輪廓保持度高的低頻背景圖像。然后為了使圖像高頻部分的雨線信息更為明顯,減少雨線特征學(xué)習(xí)時高頻圖像中的背景誤判,將得到的高頻雨線圖像再一次通過濾波器得到減弱背景信息同時增強雨線信息的到更高頻雨線圖像。其次針對低頻背景圖像上也殘留了大量雨痕,該文提出將低頻背景圖像和更高頻雨線圖像一起送入卷積神經(jīng)網(wǎng)絡(luò)進行特征學(xué)習(xí),其中對圖像提取的是多尺度特征信息,最后得到雨線去除更徹底的復(fù)原圖像。同時在構(gòu)造網(wǎng)絡(luò)模型時利用空洞卷積代替標準卷積來提取圖像的特征信息,得到更豐富的圖像特征,提高了算法的去雨性能。從實驗結(jié)果可以看出去雨之后的圖像清晰,細節(jié)保持度較高。
  • 圖  1  不同濾波器濾波后得到的低頻背景圖像對比

    圖  2  低頻圖像對比

    圖  3  顏色直方圖對比

    圖  4  高頻雨線圖像與更高頻雨線圖像對比

    圖  5  多通道多尺度卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)

    圖  6  模擬雨圖去雨效果對比

    圖  7  真實雨圖去雨效果對比

    表  1  PSNR對比結(jié)果

    圖像文獻[8]文獻[12]文獻[13]本文方法
    第1幅20.15721.23321.77923.128
    第2幅21.53024.78325.63726.821
    第3幅27.00630.25231.55334.460
    300張平均22.53724.83826.09627.794
    下載: 導(dǎo)出CSV

    表  2  SSIM對比結(jié)果

    圖像文獻[8]文獻[12]文獻[13]本文方法
    第1幅0.90120.90480.91330.9236
    第2幅0.89370.91560.92480.9420
    第3幅0.88300.93640.94300.9448
    300張平均0.89630.92460.93980.9434
    下載: 導(dǎo)出CSV

    表  3  圖像質(zhì)量指標對比

    圖像指標單尺度卷積
    5 × 5
    單尺度卷積
    7 × 7
    多尺度卷積
    第1幅PSNR21.25320.19723.128
    SSIM0.91090.91140.9236
    第2幅PSNR25.01725.71426.821
    SSIM0.92110.92370.9420
    第3幅PSNR31.58130.33634.460
    SSIM0.93820.93690.9448
    300張平均PSNR25.97325.28527.794
    SSIM0.93500.93370.9434
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2019-09-29
  • 修回日期:  2020-05-28
  • 網(wǎng)絡(luò)出版日期:  2020-07-13
  • 刊出日期:  2020-09-27

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