| Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences |
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| 作者姓名： | 李伟锋 郁道银 陈晓冬 |
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| 作者单位： | School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，Tianjin 300072 China，Tianjin 300072 China，Tianjin 300072 China |
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| 摘 要： | In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.
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| 关 键 词： | 自适应加权均数 运动轨迹 噪声图象序列 改良滤波算法 |
| 收稿时间： | 2007-01-17 |
Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences |
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| LI Weifeng,YU Daoyin,CHEN Xiaodong.Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences[J].Transactions of Tianjin University,2007,13(2):103-106. |
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| Authors: | LI Weifeng YU Daoyin CHEN Xiaodong |
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| Abstract: | In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering. |
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| Keywords: | adaptive weighted averaging image sequences motion trajectory noise variance |
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