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Infrared and visible image fusion based on deep Boltzmann model

Feng Xin Li Chuan Hu Kai-Qun

Infrared and visible image fusion based on deep Boltzmann model

Feng Xin, Li Chuan, Hu Kai-Qun
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  • In the infrared and visible light image fusion, the noise interference always exists. There is also the disadvantage that image fusion is easy to produce artifacts which cause blurred edge and low contrast. In order to solve these problems, in this study we propose an image fusion method based on deep model segmentation. First of all, deep Bolzmann machine is adopted to learn prior target and background contour and construct a contour deep segmentation model. After the optimal energy segmentation, Split Bregman iteration is used to obtain the infrared and visible image contour. Then non-subsampled contourlet transform is adopted to decompose the source images. The segmented background contour coefficients are fused by the structure similarity rule. Finally, the fused image is reconstructed by the non-subsampled contourlet inverse transform. The experimental results show that this algorithm can effectively obtain fused images with clear target contour and background contour. The fused images also have high contrast and low noise. The results show that it is an effective method of achieving the infrared and visible image fusion.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 51375517), Chongqing University Innovation Team Project, China (Grant No. KJTD201313), the Dr Campus youth fund of Chongqing Technology and Business University, China (Grant No. 1352007), and the Natural Science Foundation of Chongqing City Board of Education, China (Grant No. KJ1400628).
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    Wang J, Peng J Y, He G Q 2013 Acta Armament. 34 815(in Chinese)[王珺, 彭进业, 何贵青 2013 兵工学报 34 815]

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    Gan T, Feng S T, Nie S P 2011 Acta Phys. Sin. 60 114205(in Chinese)[甘甜, 冯少彤, 聂守平 2011 物理学报 60 114205]

    [8]

    Kong W, Lei Y 2011 IET Signal Processing 5 75

    [9]

    Shen Y, Dang J W, Feng X 2013 Spectroscopy and Spectral Analysis 33 1506(in Chinese)[沈瑜, 党建武, 冯鑫 2013 光谱学与光谱分析 33 1506]

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    da Cunha A L, Zhou J P, Do M N 2006 IEEE Trans. Image Process. 15 3089

    [17]

    Le Q V, Ngiam J, Coates A, Ng A Y 2011 28th Int. Conf. Machine Learning Bellevue Washington, USA, June 28, 2011 p1209

    [18]

    Zhang Q, Guo B L 2007 J. Infrared Millim. Waves 26 185 (in Chinese)[张强, 郭宝龙 2007 红外与毫米波学报 26 185]

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    Li X, Qin Y 2011 IET Image Process 5 141

  • [1]

    Zhang C, Bai L F, Zhang Y 2007 Acta Phys. Sin. 56 3227(in Chinese)[张闯, 柏连发, 张毅 2007 物理学报 56 3227]

    [2]

    Zhao L Y, Ma Q L, Li X R 2012 Acta Phys. Sin. 61 194204(in Chinese)[赵辽英, 马启良, 厉小润 2012 物理学报 61 194204]

    [3]

    Ma J F, Hou K, Bao S L, Chen C 2011 Chin. Phys. B 20 028701

    [4]

    Wang X Y, Wang Y X, Yun J J 2011 Chin. Phys. B 20 104202

    [5]

    Zheng H, Zheng C, Yan X S 2012 Chin. J. Sci. Instrum. 33 1613(in Chinese)[郑虹, 郑晨, 闫秀生 2012 仪器仪表学报 33 1613]

    [6]

    Wang J, Peng J Y, He G Q 2013 Acta Armament. 34 815(in Chinese)[王珺, 彭进业, 何贵青 2013 兵工学报 34 815]

    [7]

    Gan T, Feng S T, Nie S P 2011 Acta Phys. Sin. 60 114205(in Chinese)[甘甜, 冯少彤, 聂守平 2011 物理学报 60 114205]

    [8]

    Kong W, Lei Y 2011 IET Signal Processing 5 75

    [9]

    Shen Y, Dang J W, Feng X 2013 Spectroscopy and Spectral Analysis 33 1506(in Chinese)[沈瑜, 党建武, 冯鑫 2013 光谱学与光谱分析 33 1506]

    [10]

    Mohamed A, Dahl G, Hinton G 2011 IEEE Trans. Audio, Speech, and Language Process. 12 134

    [11]

    Tang Y 2010 In NIPS Workshop on Transfer Learning by Learning Rich Generative Models Vancouver, B C, Canada, December 6, 2010 p2202

    [12]

    Eslami S M, Heess N, Winn J 2012 Computer Vision and Pattern Recognition Rhode Island Provine, America, 16 June, 2012 p983

    [13]

    Wang Z, Bovik A C 2004 IEEE Trans. Image Process. 13 600

    [14]

    Goldstein T, Bresson X, Osher S 2010 J. Sci. Comput. 45 272

    [15]

    Latech L, Lakamper R, Eckhardt U 2000 IEEE Conference on Computer Vision and Pattern Recognition Hilton Head, June 13, USA, 2000 p424

    [16]

    da Cunha A L, Zhou J P, Do M N 2006 IEEE Trans. Image Process. 15 3089

    [17]

    Le Q V, Ngiam J, Coates A, Ng A Y 2011 28th Int. Conf. Machine Learning Bellevue Washington, USA, June 28, 2011 p1209

    [18]

    Zhang Q, Guo B L 2007 J. Infrared Millim. Waves 26 185 (in Chinese)[张强, 郭宝龙 2007 红外与毫米波学报 26 185]

    [19]

    Li X, Qin Y 2011 IET Image Process 5 141

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  • Received Date:  19 February 2014
  • Accepted Date:  01 May 2014
  • Published Online:  20 September 2014

Infrared and visible image fusion based on deep Boltzmann model

  • 1. College of Mechanical Engineering, Key Laboratory of Manufacturing Equipment Mechanism Design and Control of Chongqing, Chongqing Technology and Business University, Chongqing 400067, China
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant No. 51375517), Chongqing University Innovation Team Project, China (Grant No. KJTD201313), the Dr Campus youth fund of Chongqing Technology and Business University, China (Grant No. 1352007), and the Natural Science Foundation of Chongqing City Board of Education, China (Grant No. KJ1400628).

Abstract: In the infrared and visible light image fusion, the noise interference always exists. There is also the disadvantage that image fusion is easy to produce artifacts which cause blurred edge and low contrast. In order to solve these problems, in this study we propose an image fusion method based on deep model segmentation. First of all, deep Bolzmann machine is adopted to learn prior target and background contour and construct a contour deep segmentation model. After the optimal energy segmentation, Split Bregman iteration is used to obtain the infrared and visible image contour. Then non-subsampled contourlet transform is adopted to decompose the source images. The segmented background contour coefficients are fused by the structure similarity rule. Finally, the fused image is reconstructed by the non-subsampled contourlet inverse transform. The experimental results show that this algorithm can effectively obtain fused images with clear target contour and background contour. The fused images also have high contrast and low noise. The results show that it is an effective method of achieving the infrared and visible image fusion.

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