透火焰红外数字全息图像的分辨率增强算法

柴金燕, 黄晁, 陈春燕, 等. 透火焰红外数字全息图像的分辨率增强算法[J]. 光电工程, 2019, 46(4): 180418. doi: 10.12086/oee.2019.180418
引用本文: 柴金燕, 黄晁, 陈春燕, 等. 透火焰红外数字全息图像的分辨率增强算法[J]. 光电工程, 2019, 46(4): 180418. doi: 10.12086/oee.2019.180418
Chai Jinyan, Huang Chao, Cheng Chunyan, et al. Resolution enhancement algorithm based on infrared digital holography imaging through flame[J]. Opto-Electronic Engineering, 2019, 46(4): 180418. doi: 10.12086/oee.2019.180418
Citation: Chai Jinyan, Huang Chao, Cheng Chunyan, et al. Resolution enhancement algorithm based on infrared digital holography imaging through flame[J]. Opto-Electronic Engineering, 2019, 46(4): 180418. doi: 10.12086/oee.2019.180418

透火焰红外数字全息图像的分辨率增强算法

  • 基金项目:
    国家重点研发计划(2016YFB0700501);宁波国家高新区(新材料科技城)重大科技专项(重大技术创新项目);江苏省研究生科研与实践创新计划项目(SJCX17_0233)
详细信息
    作者简介:
    通讯作者: 黄晁(1972-),男,博士,副研究员,主要从事智能感知技术的研究。E-mail:chuang@ict.ac.cn
  • 中图分类号: TP391; TB872

Resolution enhancement algorithm based on infrared digital holography imaging through flame

  • Fund Project: Supported by National Key Research and Development Plan (2016YFB0700501), Major National Science and Technology Project (Major Technology Innovation Project) of Ningbo National High-tech Zone (New Materials Science and Technology City), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX17_0233)
More Information
  • 近年来,高温干扰遮蔽情况下利用红外热成像与数字全息成像相结合的新技术观察火场中目标成为时下的研究重点。理论上火焰和浓烟对长波长的红外数字全息成像没有影响,但在现实火场环境中,燃烧物的大颗粒灰尘将会干扰光路,严重增加了全息图重建图像的噪声。本文提出了一种新的图像处理算法来抑制红外数字全息重建的噪声。该算法利用双边滤波器配合拉普拉斯金字塔算法将全息重建图像的细节和能量层分开,再对细节层进行滤波,然后用反向拉普拉斯金字塔算法将分离的层叠加回重建图像中,从而提高重建像的分辨率,并通过模拟火场环境验证了该算法对改善红外数字全息图重建像的分辨率具有显著效果。

  • Overview: In general, the target observation methods are mostly concentrated in the visible and infrared light bands, and the target and scene are observed by the imaging devices of the corresponding bands respectively. However, in areas where fires occur, the mere use of traditional imaging observations does not meet the requirements. On the one hand, due to the shielding effect of fire and smoke, the short-wavelength scattering effect in the visible light band is obvious, and it is not suitable for such turbid medium. Therefore, it is almost impossible to see the target situation in the fire field by means of visible light observation, which brings extremes to search and rescue. On the other hand, the use of infrared thermal imaging alone has certain disadvantages. Although long-wave infrared light can transmit dust and haze to a certain extent, its fundamental infrared thermal imaging is a kind of temperature difference imaging. If the target temperature is too high in the observed scene, it will cause the pixel response on the focal plane. The saturation makes it impossible to observe the target normally. In recent years, the use of new technologies combining infrared thermal imaging and digital holographic imaging to observe the target in the fire field has become the focus of research. In theory, flame and smoke have no effect on long-wavelength infrared digital holography, but in the real fire environment, large particles of dust from the combustion will interfere with the light path, which seriously increases the noise of the hologram reconstructed image. Our optical device is based on simple Mach-Zender interference. No special equipment is needed in the optical device. After collecting the interference fringe image of the imaging target and reconstructing the image, a new image processing algorithm is proposed to suppress the noise of infrared digital holographic reconstruction. The algorithm uses a bilateral filter combined with the Laplacian pyramid algorithm to separate the details and energy layers of the holographic reconstructed image, filters the detail layer, and then superimposes the separated layers back into the reconstructed image by the inverse Laplacian pyramid algorithm. In order to improve the resolution of the reconstructed image, and by simulating the fire field environment, the algorithm can effectively enhance the image resolution of the transparent digital smoke holographic image. The algorithm has the advantages of simple calculation and easy implementation, which can greatly reduce speckle noise and improve the details of reconstructed images. Experimental results show that this new algorithm has good performance.

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  • 图 1  模拟火场成像效果。(a)成像物体;(b)透火焰成像;(c)透浓烟成像;(d)透火焰红外热像仪成像

    Figure 1.  Simulating the fire field imaging effect. (a) Imaging objects; (b) Imaging through flame; (c) Imaging through thick smoke; (d) Imaging with a thermal imaging camera through a flame

    图 2  基于简单的Mach-Zender干涉的光学装置

    Figure 2.  Optical device based on simple Mach-Zender interference

    图 3  干涉条纹图。(a)无遮挡下的干涉条纹图; (b)透火焰浓烟下的干涉条纹图

    Figure 3.  Interference fringe pattern. (a) Hologram without obstruction; (b) Hologram obtained by flame and smoke

    图 4  全息图重建像。(a)无遮挡下的重建像; (b)透火焰浓烟下的重建像

    Figure 4.  Reconstruction of hologram. (a) Reconstruction of hologram without obstruction; (b) Reconstruction of holograms through flames and smoke

    图 5  金字塔分层结果。(a)高斯金字塔;(b)拉普拉斯金字塔

    Figure 5.  Pyramid stratification results. (a) Gaussian pyramid; (b) Laplacian pyramid

    图 6  金字塔分层算法流程

    Figure 6.  Pyramid layering algorithm flow

    图 7  新金字塔分层结果。(a)高斯金字塔;(b)双边滤波金字塔

    Figure 7.  New pyramid layered results. (a) Gaussian pyramid; (b) Bilateral filtering pyramid

    图 8  透火焰浓烟重建像

    Figure 8.  Reconstruction of the smoke through the flame

    图 9  对原始重建图像的处理结果。(a)透火焰和浓烟成像的原始全息图;(b)拉普拉斯金字塔算法;(c)本文提出的算法

    Figure 9.  Processing results of the original reconstructed image. (a) Hologram obtained by flame and smoke; (b) Laplace Pyramid algorithm; (c) Algorithm proposed in this paper

    图 10  透火焰成像原始图片

    Figure 10.  Through flame imaging original picture

    图 11  多种算法处理下的透火焰成像图。(a)多图片取均值算法[4];(b)自适应直方图算法[18];(c)基于特征分析的红外图像边缘增强算法[19];(d)本文提出的算法

    Figure 11.  Through flame imaging image processed by various algorithms. (a) Multiple images are cumulatively averaged[4]; (b) Adaptive histogram equalization algorithm[18]; (c) Edge enhancement and noise suppression for infrared image based on feature analysis[19]; (d) Algorithm proposed in this paper

    表 1  不同算法的PSNR对比

    Table 1.  PSNR contrast of different algorithms

    名称 PSNR
    透火焰成像原始图片 7.8265
    多图片取均值[4] 8.6489
    IGM配合PCNN[18] 20.4698
    基于特征分析的红外图像边缘增强[19] 16.5462
    本文算法 27.6489
    下载: 导出CSV
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出版历程
收稿日期:  2018-08-01
修回日期:  2018-12-08
刊出日期:  2019-04-01

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