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论文:2020,Vol:38,Issue(4):723-732 |
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引用本文： |
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王晓柱, 钮赛赛, 张凯, 印剑飞, 闫杰. 基于小波变换与特征提取的红外弱小目标图像融合[J]. 西北工业大学学报 |
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WANG Xiaozhu, NIU Saisai, ZHANG Kai, YIN Jianfei, YAN Jie. Image Fusion of Infrared Weak-Small Target Based on Wavelet Transform and Feature Extraction[J]. Northwestern polytechnical university |
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| 基于小波变换与特征提取的红外弱小目标图像融合 |
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| 王晓柱1, 钮赛赛2, 张凯1, 印剑飞2, 闫杰1 |
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1. 西北工业大学 航天学院, 陕西 西安 710072;
2. 上海航天技术研究院, 上海 201109 |
| 摘要: |
| 当前红外单波段数据不能全面反映图像细节以及轮廓信息，弱小目标成像后难以抵抗背景干扰，使得图像产生较低的信噪比。因此有必要利用不同波段数据的纹理差异性，通过互补融合方法提高图像的信噪比。基于此，提出一种基于小波变换与特征提取的融合方法。首先将多源图像进行多尺度二维分解，获得各图像的低频信息与高频信息，在此基础上，高频信息采取绝对值取大的融合方法，低频信息采取加权求平均的融合方法，进而重构图像。然后，利用特征提取方法得到中波与长波特征图像。最后对重构图像与红外中长波特征图像进行对比度调制再融合。融合结果与多种融合算法进行对比。实验结果表明，该算法能够增强图像中弱小目标的灰度，可以很好地识别目标，解决了图像中弱小目标抗背景干扰的问题。 |
| 关键词:
红外双波段融合
弱小目标
小波变换
特征提取
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| Image Fusion of Infrared Weak-Small Target Based on Wavelet Transform and Feature Extraction |
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| WANG Xiaozhu1, NIU Saisai2, ZHANG Kai1, YIN Jianfei2, YAN Jie1 |
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1. School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Shanghai Institute of Spaceflight Control Technology, Shanghai 201109, China |
| Abstract: |
| The image details and contour information cannot be fully reflected for the current infrared single-band data. It is difficult for the weak-small target to resist background interference after imaging, so that the image produces a low ratio of signal-to-noise. Therefore, it is necessary to use the texture difference of different band data to improve the signal-to-noise ratio of the image by using the complementary fusion method. Based on the above-mentioned, a fusion method based on wavelet transform and feature extraction is proposed. Firstly, the source images are multi-scale and two-dimensionally decomposed to obtain low-frequency information and high-frequency information. And that, the high-frequency information adopt the method of maximizing the absolute value, the low-frequency information adopt the method of weighted averaging, and reconstruct the image. Then, the infrared feature extraction method is used to obtain the medium wave and long wave feature images. Finally, the reconstructed image is contrast-modulated and refused with the medium-long wave infrared feature image. The fusion results are compared with a variety of fusion algorithms. The experimental results show that the algorithm can enhance the gray scale of weak-small targets in the image, which can identify the target well and solve the problem of weak target against background interference in infrared images. |
| Key words:
infrared dual-band fusion
weak-small target
wavelet transform
feature extraction
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| 收稿日期: 2019-09-29
修回日期:
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| DOI: 10.1051/jnwpu/20203840723 |
| 基金项目: 国家自然科学基金（61703337）与上海航天科技创新基金（SAST2017-082）资助 |
| 通讯作者:
Email： |
| 作者简介: 王晓柱(1990-),西北工业大学博士研究生,主要从事红外目标特性分析与多波段融合技术研究。
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| 作者相关文章 |
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| 王晓柱 在本刊中的所有文章 |
| 钮赛赛 在本刊中的所有文章 |
| 张凯 在本刊中的所有文章 |
| 印剑飞 在本刊中的所有文章 |
| 闫杰 在本刊中的所有文章 |
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参考文献: |
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[1] PAN Y, XU X, QIAO Y. Design of Two-DMD Based Zoom MW and LW Dual-Band IRSP Using Pixel Fusion[J]. Infrared Physics & Technology, 2018, 91:90-100
[2] LUO X, LI X, WANG P, et al. Infrared and Visible Image Fusion Based on NSCT and Stacked Sparse Auto Encoders[J]. Multimedia Tools Applications, 2018, 77(17):22407-22431
[3] BEN H A, YUN H, HAMID K, et al. A Multi-Scale Approach to Pixel-Level Image Fusion[J]. Integrated Computer-Aided Engineering, 2005, 12(2):135-146
[4] JIANG Q, JIN X, LEE S J, et al. A Novel Multi-Focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets[J]. IEEE Access, 2017, 5:20286-20302
[5] WANG L, LI B, TIAN L F. EGGDD:an Explicit Dependency Model for Multi-Modal Medical Image Fusion in Shift-Invariant Shearlet Transform Domain[J]. Information Fusion, 2014,19:29-37
[6] QU X, ZHANG F, ZHANG Y, et al. A Method of Dual-Band Infrared Images Fusion Based on Gradient Pyramid Decomposition[C]//IET International Conference on Information Science and Control Engineering, 2012
[7] SUN Y Q, TIAN J W, LIU J. Dim Small Targets Detection Based on Dual band Infrared Image Fusion[C]//IEEE International Conference on Industrial Technology, 2016
[8] 王文博, 王英瑞. 红外双波段点目标双色比分析与处理[J]. 红外与激光工程,2015,44(8):2347-2350 WANG Wenbo, WANG Yingrui. Analysis and Processing of Infrared Dual Waveband Radiation Ratio Based Point Target[J]. Infrared and laser Engineering, 2015, 44(8):2347-2350(in Chinese)
[9] 郭雷, 程塨, 赵天云. 基于小波变换和邻域特征的多聚焦图像融合算法[J]. 西北工业大学学报, 2011, 29(3):454-459 GUO Lei, CHENG Gong, ZHAO Tianyun. A New and Effective Multi-Focus Image Fusion Algorithm Based on Wavelet Transforms and Neighborhood Features[J].Journal of Northwestern Polytechnical University, 2011, 29(3):454-459(in Chinese)
[10] ZHANG Y, ZHANG L, BAI X, et al. Infrared and Visual Image Fusion through Infrared Feature Extraction and Visual Information Preservation[J]. Infrared Physics & Technology, 2017, 83:227-237
[11] JIN X, JIANG Q, YAO S, et al. Infrared and Visual Image Fusion Method Based on Discrete Cosine Transform and Local Spatial Frequency in Discrete Stationary Wavelet Transform Domain[J]. Infrared Physics & Technology, 2017, 88:1-12
[12] 李秋华,王厚生,邹自力. 基于小波变换与灰度形态学滤波的双波段红外图像弱目标融合检测[J]. 信号处理,2006,22(3):312-316 LI Qiuhua, WANG Housheng, ZOU Zili. Detection of Dual Band IR Small Target Fusion Detection Based on Wavelet Trans formation and Grayscale Morphology Filtering[J]. Signal Processing, 2006, 22(3):312-316(in Chinese)
[13] 张生伟, 李伟, 赵雪景. 一种基于稀疏表示的可见光与红外图像融合方法[J]. 电光与控制,2017,6:51-56 ZHANG Shengwei, LI Wei, ZHAO Xuejing. A Method for Fusion of Visible and Infrared Images Based on Sparse Representation[J]. Electronics Optics & Control, 2017, 6:51-56(in Chinese)
[14] 郭雷, 刘坤. 基于非下采样Contourlet变换的自适应图像融合算法[J]. 西北工业大学学报, 2009, 27(2):255-259 GUO Lei, LIU Kun. Applying NSCT(Non Subsampled Contourlet Transform) Theory to Achieving Effective Image Fusion[J]. Journal of Northwestern Polytechnical University, 2009, 27(2):255-259(in Chinese)
[15] LIU Y, CHEN X, CHENG J, et al. Infrared and Visible Image Fusion with Convolutional Neural Net-Works[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2018, 16(3):1884-2021
[16] LI H, WU X J, DURRANI T S. Infrared and Visible Image Fusion with ResNet and Zero-Phase Component Analysis[J]. Information Fusion, 2018, 102:1-21
[17] LI W, CHENG Y, SUN Y, et al. Multi-Focus Image Performance Evaluation Method Based on the Extraction and Combination of Multiple Metrics[C]//International Conference on Computational Intelligence and Security, 2017
[18] SREEJA P, HARIHARAN S. An Improved Feature Based Image Fusion Technique for Enhancement of Liver Lesions[J]. Bio cybern Biomed, 2018, 38(3):611-623
[19] SMEELEN M A, SCHWERING P B W, TOET A, et al. Semi-Hidden Target Recognition in Gated Viewer Images Fused with Thermal IR Images[J]. Information Fusion, 2014, 18:131-147
[20] HE K, NIU J H, SHEN C N, et al. Image in Painting Algorithm with Adaptive Patch Using SSIM[J]. Journal of Tianjin University, 2018, 51(7):763-767
[21] TANIA R, SANTOS R, JOAO M, et al. Application for Older Adults to Ask for Help from Volunteers Through television:Design and Evaluation of a High Visual-Fidelity Prototype[C]//Applications and Vsability of Interactive Television 6th Iberoamerican Conference, 2017 |
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