搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于深度玻尔兹曼模型的红外与可见光图像融合

冯鑫 李川 胡开群

引用本文:
Citation:

基于深度玻尔兹曼模型的红外与可见光图像融合

冯鑫, 李川, 胡开群

Infrared and visible image fusion based on deep Boltzmann model

Feng Xin, Li Chuan, Hu Kai-Qun
PDF
导出引用
  • 为了克服红外与可见光图像融合时噪声干扰及易产生伪影导致目标轮廓不鲜明、对比度低的缺点,提出一种基于深度模型分割的图像融合方法. 首先,采用深度玻尔兹曼机学习红外与可见光的目标和背景轮廓先验,构建轮廓的深度分割模型,通过Split Bregman迭代算法获取最优能量分割后的红外与可见光图像轮廓;然后再使用非下采样轮廓波变换对源图像进行分解,并针对所分割的背景轮廓采用结构相似度的规则进行系数组合;最后进行非下采样轮廓波反变换重构出融合图像. 数值试验证明,该算法可以有效获取目标和背景轮廓均清晰的融合图像,融合结果不但具有较高的对比度,还能抑制噪声影响,具有有效性.
    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.
    • 基金项目: 国家自然科学基金(批准号:51375517)、重庆高校创新团队项目(批准号:KJTD201313)、重庆工商大学校内青年博士基金(批准号:1352007)和重庆市教委自然科学基金(批准号:KJ1400628)资助的课题.
    • 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).
    [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

  • [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

  • [1] 赵地, 赵莉芝, 甘永进, 覃斌毅. 基于支撑先验与深度图像先验的无预训练磁共振图像重建方法. 物理学报, 2022, 71(5): 058701. doi: 10.7498/aps.71.20211761
    [2] 张航瑛, 王雪琦, 王华英, 曹良才. 基于明度分量的Retinex-Net图像增强改进方法. 物理学报, 2022, 71(11): 110701. doi: 10.7498/aps.71.20220099
    [3] 赵智鹏, 周双, 王兴元. 基于深度学习的新混沌信号及其在图像加密中的应用. 物理学报, 2021, 70(23): 230502. doi: 10.7498/aps.70.20210561
    [4] 赵地, 赵莉芝, 甘永进, 覃斌毅. 基于支撑先验与深度图像先验的无预训练磁共振图像重建方法. 物理学报, 2021, (): . doi: 10.7498/aps.70.20211761
    [5] 张士杰, 王颖明, 王琦, 李晨宇, 李日. 基于元胞自动机-格子玻尔兹曼模型的枝晶碰撞行为模拟. 物理学报, 2021, 70(23): 238101. doi: 10.7498/aps.70.20211292
    [6] 张乾毅, 韦华健, 李华兵. 基于晶格玻尔兹曼方法的多段淋巴管模型. 物理学报, 2021, 70(21): 210501. doi: 10.7498/aps.70.20210514
    [7] 宋强, 孙晓兵, 刘晓, 提汝芳, 黄红莲, 王昊. 基于偏振信息探究水下环境气泡群对目标成像的影响. 物理学报, 2021, 70(14): 144201. doi: 10.7498/aps.70.20202152
    [8] 周静, 张晓芳, 赵延庚. 一种基于图像融合和卷积神经网络的相位恢复方法. 物理学报, 2021, 70(5): 054201. doi: 10.7498/aps.70.20201362
    [9] 陈炜, 郭媛, 敬世伟. 基于深度学习压缩感知与复合混沌系统的通用图像加密算法. 物理学报, 2020, 69(24): 240502. doi: 10.7498/aps.69.20201019
    [10] 郎利影, 陆佳磊, 于娜娜, 席思星, 王雪光, 张雷, 焦小雪. 基于深度学习的联合变换相关器光学图像加密系统去噪方法. 物理学报, 2020, 69(24): 244204. doi: 10.7498/aps.69.20200805
    [11] 杨卓群, 吴亚波, 鲁军旺, 张成园, 张雪. Lifshitz时空s波超导模型的关联长度和穿透深度. 物理学报, 2016, 65(4): 040401. doi: 10.7498/aps.65.040401
    [12] 蒋燕华, 陈佳民, 施娟, 周锦阳, 李华兵. 三角波脉动流通栓的晶格玻尔兹曼方法模型. 物理学报, 2016, 65(7): 074701. doi: 10.7498/aps.65.074701
    [13] 刘飞飞, 魏守水, 魏长智, 任晓飞. 基于速度源修正的浸入边界-晶格玻尔兹曼法研究仿生微流体驱动模型. 物理学报, 2014, 63(19): 194704. doi: 10.7498/aps.63.194704
    [14] 周锦阳, 施娟, 陈佳民, 李华兵. 脉动流血液通栓的晶格玻尔兹曼模型. 物理学报, 2014, 63(19): 194701. doi: 10.7498/aps.63.194701
    [15] 赵文达, 赵建, 续志军. 基于结构张量的变分多源图像融合. 物理学报, 2013, 62(21): 214204. doi: 10.7498/aps.62.214204
    [16] 赵辽英, 马启良, 厉小润. 基于HIS 小波变换和MOPSO的全色与多光谱图像融合. 物理学报, 2012, 61(19): 194204. doi: 10.7498/aps.61.194204
    [17] 甘甜, 冯少彤, 聂守平, 朱竹青. 基于分块DCT变换编码的小波域多幅图像融合算法. 物理学报, 2011, 60(11): 114205. doi: 10.7498/aps.60.114205
    [18] 邓敏艺, 施 娟, 李华兵, 孔令江, 刘慕仁. 用晶格玻尔兹曼方法研究螺旋波的产生机制和演化行为. 物理学报, 2007, 56(4): 2012-2017. doi: 10.7498/aps.56.2012
    [19] 张 闯, 柏连发, 张 毅. 基于灰度空间相关性的双谱微光图像融合方法. 物理学报, 2007, 56(6): 3227-3233. doi: 10.7498/aps.56.3227
    [20] 何祚庥, 张肇西, 谢诒成. 层子模型和高能电子深度非弹性散射. 物理学报, 1975, 24(2): 115-123. doi: 10.7498/aps.24.115
计量
  • 文章访问数:  5238
  • PDF下载量:  6638
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-02-19
  • 修回日期:  2014-05-01
  • 刊出日期:  2014-09-05

/

返回文章
返回