基于深度置信網(wǎng)絡(luò)的隨機(jī)脈沖噪聲快速檢測算法
doi: 10.11999/JEIT180558
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南昌大學(xué)信息工程學(xué)院 ??南昌 ??330031
A Fast Random-valued Impulse Noise Detection Algorithm Based on Deep Belief Network
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School of Information Engineering, Nanchang University, Nanchang 330031, China
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摘要:
為提高現(xiàn)有隨機(jī)脈沖噪聲(RVIN)檢測算法的檢測準(zhǔn)確率和執(zhí)行效率,該文試圖從構(gòu)建描述能力更強(qiáng)的特征矢量和訓(xùn)練非線性映射更為準(zhǔn)確的預(yù)測模型兩個方面入手,實(shí)現(xiàn)一種基于訓(xùn)練策略的快速RVIN檢測算法。一方面,提取多個不同階的對數(shù)絕對差值排序統(tǒng)計(jì)值并結(jié)合一個能夠反映圖像邊緣特性的統(tǒng)計(jì)值作為刻畫圖塊中心像素點(diǎn)是否為噪聲的特征矢量。在計(jì)算量增加極少的情況下,顯著提升了特征矢量的描述能力。另一方面,基于深度置信網(wǎng)絡(luò)(DBN)訓(xùn)練RVIN預(yù)測模型(RVIN檢測器)將特征矢量映射為噪聲類型標(biāo)簽,實(shí)現(xiàn)了比淺層預(yù)測模型更為準(zhǔn)確的映射。大量實(shí)驗(yàn)數(shù)據(jù)表明:與現(xiàn)有的RVIN檢測算法相比,所提算法在檢測準(zhǔn)確率和執(zhí)行效率兩個方面都更有優(yōu)勢。
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關(guān)鍵詞:
- 隨機(jī)脈沖噪聲 /
- 噪聲檢測 /
- 圖像局部統(tǒng)計(jì)值 /
- 深度置信網(wǎng)絡(luò) /
- 計(jì)算效率
Abstract:To improve the detection accuracy and execution efficiency of the existing Random-Valued Impulse Noise (RVIN) detectors, a fast training-based RVIN detection algorithm is implemented by constructing a more descriptive feature vector and training a detection model with more accurate nonlinear mapping. On the one hand, multiple Rank-Ordered Logarithmic absolute Deviation (ROLD) statistics are extracted and combined with a statistical value reflecting the edge characteristics in the form of feature vector to describe how RVIN-like the center pixel of a patch is. The description ability of the feature vector is improved significantly while the computational complexity is just increased in small amount. On the other hand, an RVIN prediction model (RVIN detector) is obtained by training a Deep Belief Network (DBN) to map the feature vectors to noise labels, which is more accurate than the shallow prediction model. Extensive experimental results show that, compared with the existing RVIN detectors, the proposed one has better performance in terms of detection accuracy and execution efficiency.
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表 1 圖1中a1和b1圖塊上所提取的前m階ROLD值比較
圖塊 階數(shù)m 1 2 3 4 5 6 7 8 9 10 11 12 a1 1.63 3.29 4.99 6.68 8.37 10.09 11.80 13.52 15.24 16.98 18.72 20.46 b1 1.00 2.20 3.66 5.12 6.84 8.62 10.50 12.44 14.40 16.40 18.42 20.45 下載: 導(dǎo)出CSV
表 2 各噪聲檢測算法在常用圖像集合上的各項(xiàng)性能指標(biāo)的平均值比較
方法 含噪20% 含噪40% 含噪60% 漏檢數(shù) 誤檢數(shù) 錯檢總數(shù) MEMH 漏檢數(shù) 誤檢數(shù) 錯檢總數(shù) MEMH 漏檢數(shù) 誤檢數(shù) 錯檢總數(shù) MEMH ASWM 3462 10687 14149 14.23 7478 10005 17483 16.40 14720 9804 24524 23.17 PSMF 10695 3585 14279 15.14 23038 3603 26641 30.27 39096 5634 44730 45.81 ROLD-EPR 6567 5106 11673 18.77 9462 8956 18419 15.95 10417 11616 22034 14.04 ROR-NLM 5068 9354 14421 14.91 11906 8873 20779 17.13 22553 12856 35408 23.60 MLP-EPR 8505 2081 10586 22.78 13244 5759 19003 18.09 15017 10113 25130 16.18 本文方法 4084 5909 9992 11.77 7975 8586 16561 12.17 10076 12594 22670 11.53 下載: 導(dǎo)出CSV
表 3 各檢測算法統(tǒng)一用相同修復(fù)算法降噪后在PSNR指標(biāo)上的比較(dB)
方法 含噪20% 含噪40% 含噪50% 含噪60% Lena House Bridge Lena House Bridge Lena House Bridge Lena House Bridge ASWM 39.06 33.31 25.76 34.27 31.21 24.33 30.66 28.81 23.25 26.04 26.13 21.61 PSMF 30.24 27.82 23.25 29.26 26.03 22.77 26.03 24.09 21.91 22.04 21.98 20.00 ROLD-EPR 34.77 33.31 26.75 31.77 31.21 24.25 30.54 28.81 23.12 28.78 26.13 22.20 ROR-NLM 36.94 28.92 25.28 31.58 28.91 23.59 27.61 27.39 22.35 22.92 24.68 20.39 MLP--EPR 36.45 39.36 27.71 33.83 37.49 24.40 31.86 36.48 23.33 29.42 33.96 22.25 本文方法 40.35 42.95 26.97 35.89 40.31 24.91 33.04 38.27 23.36 29.66 36.12 22.18 下載: 導(dǎo)出CSV
表 4 各噪聲檢測算法平均執(zhí)行時(shí)間的比較(s)
方法 ASWM PSMF ROLD-EPR ROR-NLM MLP-EPR 本文方法 時(shí)間 102.72 0.86 10.40 77.19 0.79 0.70 下載: 導(dǎo)出CSV
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