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
-
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
-
School of Information Engineering, Nanchang University, Nanchang 330031, China
-
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。
-
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.
-
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
-
LIU Licheng, Chen C L P, and ZHOU Yicong. A new weighted mean filter with a two-phase detector for removing impulse noise[J]. Information Sciences, 2015, 315(September): 4052–4057. doi: 10.1016/j.ins.2015.03.067 SINGH N, THILAGAVATHY T, LAKSHMIPRIYA R T, et al. Some studies on detection and filtering algorithms for the removal of random valued impulse noise[J]. IET Image Processing, 2017, 11(11): 953–963. doi: 10.1049/iet-ipr. 2017.0346 DONG Yiqiu and XU Shufang. A new directional weighted median filter for removal of random-valued impulse noise[J]. IEEE Signal Processing Letters, 2007, 14(3): 193–196. doi: 10.1109/LSP.2006.884014 劉萬軍, 梁雪劍, 曲海成. 自適應(yīng)增強(qiáng)卷積神經(jīng)網(wǎng)絡(luò)圖像識別[J]. 中國圖象圖形學(xué)報(bào), 2017, 22(12): 1723–1736. doi: 10.11834/jig.170079LIU Wanjun, LIANG Xuejian, and QU Haicheng. Adaptively enhanced convolutional neural network algorithm for image recognition[J]. Journal of Image and Graphics, 2017, 22(12): 1723–1736. doi: 10.11834/jig.170079 于海平, 何發(fā)智, 潘一騰, 等. 一種基于多特征的距離正則化水平集快速分割方法[J]. 電子學(xué)報(bào), 2017, 45(3): 534–539. doi: 10.3969/j.issn.0372-2112.2017.003.004YU Haiping, HE Fazhi, PAN Yiteng, et al. A fast distance regularized level set method for segmentation based on multi-features[J]. Acta Electronica Sinica, 2017, 45(3): 534–539. doi: 10.3969/j.issn.0372-2112.2017.003.004 王森, 王余, 王易川, 等. 水下高速目標(biāo)聲譜圖特征提取及分類設(shè)計(jì)[J]. 電子與信息學(xué)報(bào), 2017, 39(11): 2684–2689. doi: 10.11999/JEIT170283WANG Sen, WANG Yu, WANG Yichuan, et al. Feature extraction and classification of spectrum of radiated noise of underwater high speed vehicle[J]. Journal of Electronics &Information Technology, 2017, 39(11): 2684–2689. doi: 10.11999/JEIT170283 周籮魚, 湯佳欣. 基于圖像稀疏表達(dá)的模擬退火圖像復(fù)原[J]. 光電子·激光, 2018, 29(2): 218–223. doi: 10.16136/j.joel.2018.02.0088ZHOU Luoyu and TANG Jiaxin. Image restoration based on simulated annealing with image sparse representation[J]. Journal of Optoelectronics · Laser, 2018, 29(2): 218–223. doi: 10.16136/j.joel.2018.02.0088 AKKOUL S, LEDEE R, LECONGE R, et al. A new adaptive switching median filter[J]. IEEE Signal Processing Letters, 2010, 17(6): 587–590. doi: 10.1109/LSP.2010.2048646 YU Hancheng, ZHAO Li, and WANG Haixian. An efficient procedure for removing random-valued impulse noise in images[J]. IEEE Signal Processing Letters, 2008, 15(12): 922–925. doi: 10.1109/LSP.2008.2005051 GARNETT R, HUEGERICH T, CHUI C, et al. A universal noise removal algorithm with an impulse detector[J]. IEEE Transactions on Image Processing, 2005, 14(11): 1747–1754. doi: 10.1109/TIP.2005.857261 DONG Yiqiu, RAYMOND H C, and XU Shufang. A detection statistic for random-valued impulse noise[J]. IEEE Transactions on Image Processing, 2007, 16(4): 1112–1120. doi: 10.1109/TIP.2006.891348 XIONG Bo and YIN Zhouping. A universal denoising framework with a new impulse detector and nonlocal means[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1663–1675. doi: 10.1109/TIP.2011.2172804 ROY A, SINGHA J, DEVI S S, et al. Impulse noise removal using SVM classification based fuzzy filter from gray scale images[J]. Signal Processing, 2016, 128(11): 262–273. doi: 10.1016/j.sigpro.2016.04.007 TURKMEN I. The ANN based detector to remove random-valued impulse noise in images[J]. Journal of Visual Communication and Image Representation, 2015, 34(10): 28–36. doi: 10.1016/j.jvcir.2015.10.011 SOLEIMANY S and HAMGHALAM M. A novel random-valued impulse noise detector based on MLP neural network classifier[C]. Artificial Intelligence and Robotics (IRANOPEN), Qazvin, Iran, 2017: 165–169. HINTON G, DENG Li, YU Dong, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012, 29(6): 82–97. doi: 10.1109/MSP.2012.2205597 WANG Zhou and ZHANG D. Progressive switching median filter for the removal of impulse noise highly corrupted images[J]. IEEE Transactions on Circuits System II: Analog Digital Signal Processing, 1999, 46(1): 78–80. doi: 10.1109/82.749102 CHAN S H, WANG Xiran, and ELGENDY O A. Plug-and-play ADMM for image restoration: Fixed-point convergence and applications[J]. IEEE Transactions on Computational Imaging, 2017, 3(1): 84–98. doi: 10.1109/TCI.2016.2629286 -
計(jì)量
- 文章訪問數(shù): 2932
- HTML全文瀏覽量: 934
- PDF下載量: 92
- 被引次數(shù): 0
下載:
