Image fusion algorithm based on DOG filter

Zeng Hanlin; Meng Xiangyong; Qian Weixian

doi:10.3788/IRLA20200091

Volume 49 Issue S1

Sep. 2020

Turn off MathJax

Article Contents

Article Navigation > Infrared and Laser Engineering > 2020 > 49(S1): 20200091

Zeng Hanlin, Meng Xiangyong, Qian Weixian. Image fusion algorithm based on DOG filter[J]. Infrared and Laser Engineering, 2020, 49(S1): 20200091. doi: 10.3788/IRLA20200091

Citation:

Zeng Hanlin, Meng Xiangyong, Qian Weixian. Image fusion algorithm based on DOG filter[J]. Infrared and Laser Engineering, 2020, 49(S1): 20200091. doi: 10.3788/IRLA20200091

Image fusion algorithm based on DOG filter

doi: 10.3788/IRLA20200091

1. Electronic and Optical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;
2. Qiqihar North Hua'an Group Test Site, Qiqihar 161000, China

Received Date: 2020-03-23
Rev Recd Date: 2020-06-13
Publish Date: 2020-09-22

Abstract

Image fusion is one of the important contents in the field of image processing. The traditional fusion algorithm fuses the source images and processes them according to certain rules. Although a good fusion effect can be achieved, the algorithm requires high image registration, then the fusion image also has the problem of loss of details, and the problem that the target is not obvious enough. To improve the above problems, the characteristics of the infrared image, visible image and the infrared target were analyzed, target detection was used in image fusion, and the DOG filter was used to extract the targets in the infrared image. The fusion coefficient matrix was obtained through multi-scale DOG image calculation, and then the fusion sub-map was calculated. Finally, a fusion image with obvious targets and good details was obtained,and the requirement for image registration was reduced. Five commonly used evaluation indicators, as well as the signal-to-clutter ratio and background similarity, were used to evaluate the fusion image. Experimental results show that the proposed algorithm is superior to the commonly used image fusion methods in both subjective vision and objective evaluation indicators.
- imaging fusion,
- target detection,
- DOG filter,
- signal-to-clutter ratio

References

[1]	Wang Guanglong, Tian Jie, Zhu Wenjie, et al. Feature fusion and weight adaptive updating based motion blur object tracking[J]. Optics and Precision Engineering,2019, 27(5):1158-1166. (in Chinese)王广龙, 田杰, 朱文杰, 等.特征融合和自适应权重更新相结合的运动模糊目标跟踪[J].光学精密工程, 2019, 27(5):1158-1166.
[2]	Wang Sheng. Dangerous objects detection in millimeter wave images[D]. Beijing:Tsinghua University, 2018. (in Chinese)王胜. 毫米波图像中危险物品检测[D]. 北京:清华大学, 2018.
[3]	Li Dezhi, Chen Hongda, Bi Feng, et al. Progress of multimodal molecular imaging technology in tumor diagnosis[J]. Chinese Journal of Analytical Chemistry, 2016, 44(10):1609-1618. (in Chinese)李德智, 陈宏达, 毕锋, 等.多模态分子影像技术在肿瘤诊断中的进展[J]. 分析化学, 2016, 44(10):1609-1618.
[4]	Shi Minhong. Summary of the development of image fusion technology[J]. Computer Era, 2019(9):27-29. (in Chinese)史敏红. 图像融合技术发展综述[J].计算机时代, 2019(9):27-29.
[5]	Rong Chuanzhen, Jia Yongxing, Yang Yu, et al. Image fusion based on infrared object extraction[J]. Journal of Signal Processing, 2019, 35(7):1141-1151. (in Chinese)荣传振, 贾永兴, 杨宇, 等.基于红外目标特征提取的图像融合方法[J]. 信号处理, 2019, 35(7):1141-1151.
[6]	Nan Tianzhang, Geng Jianjun, Chen Xu, et al. Infrared low, slow and small target detection based on neighborhood characteristics[J]. Infrared and Laser Engineering, 2019, 48(S1):S128002. (in Chinese)南天章, 耿建君, 陈旭, 等.基于邻域特征的红外低慢小目标检测[J]. 红外与激光工程, 2019, 48(S1):S128002.
[7]	Wu Yanfeng, Wang Yanjie, Sun Haijiang, et al. LSS-target detection in complex sky backgrounds[J]. Chinese Optics,2019, 12(4):854-866. (in Chinese)吴言枫, 王延杰, 孙海江, 等. 复杂动背景下的"低小慢"目标检测技术[J]. 中国光学, 2019, 12(4):854-866.
[8]	Lu Fuxing, Chen Xin, Chen Guilin, et al. Dim and small target detection based on background adaptive multi-feature fusion[J]. Infrared and Laser Engineering, 2019, 48(3):0326002. (in Chinese)陆福星, 陈忻, 陈桂林, 等.背景自适应的多特征融合的弱小目标检测[J]. 红外与激光工程, 2019, 48(3):0326002.
[9]	Lu Ruiqi, Ma Huimin. Template matching with multi-scale saliency[J]. Optics and Precision Engineering, 2018, 26(11):2776-2784. (in Chinese)逯睿琦, 马惠敏. 多尺度显著性区域提取的模板匹配[J]. 光学精密工程, 2018, 26(11):2776-2784.
[10]	Chen Lerui, Pan Qiuping, Kong Jinsheng. Research on image fusion based on saliency and guided filter[J]. Industrial Control Computer, 2016, 29(5):110-111. (in Chinese)陈乐瑞, 潘秋萍, 孔金生. 基于显著性和导向滤波的图像融合算法研究[J]. 工业控制计算机, 2016, 29(5):110-111.
[11]	Fu Zhizhong, Wang Xue, Li Xiaofeng, et al. Infrared and visible image fusion based on visual saliency and NSCT[J]. Journal of University of Electronic Science and Technology of China, 2017, 46(2):357-362. (in Chinese)傅志中, 王雪, 李晓峰, 等.基于视觉显著性和NSCT的红外与可见光图像融合[J]. 电子科技大学学报, 2017, 46(2):357-362.
[12]	Qiao Tieying, Cai Lihua, Li Ning, et al. Opposite target measurement based on infrared radiation characteristic system[J]. Chinese Optics, 2018, 11(5):804-811. (in Chinese)乔铁英, 蔡立华, 李宁, 等.基于红外辐射特性系统实现对面目标测量[J]. 中国光学, 2018, 11(5):804-811.
[13]	Cao Lin, Lu Hongying, Cheng Lin. Application of improved DOG operator in image edge extraction[J]. Computer Knowledge and Technology, 2018, 14(6):139-142. (in Chinese)曹麟, 鲁红英, 程琳.基于改进的DOG算子在图像边缘提取中的应用研究[J]. 电脑知识与技术, 2018, 14(6):139-142.
[14]	Birch P, Mitra B, Bangalore N M, et al. Approximate bandpass and frequency response models of the difference of Gaussian filter[J]. Optics Communications, 2010, 283(24):4942-4948.
[15]	Xia Mingge, Ouyang Wen. Pixel level image fusion methods and fusion performance evaluation[J]. Remote Sensing Technology and Application, 2002(4):224-229. (in Chinese)夏明革, 欧阳文. 像素级图像融合方法与融合效果评价[J]. 遥感技术与应用, 2002(4):224-229.
[16]	Yu Xianchuan, Pei Wenjing. Performance evaluation of image fusion quality metrics for the quality of different fusion methods[J]. Infrared and Laser Engineering, 2012, 41(12):3416-3422. (in Chinese)余先川, 裴文静.针对不同融合算法的质量评价指标性能评估[J]. 红外与激光工程, 2012, 41(12):3416-3422.

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Get Citation

PDF

XML

Article Metrics

Article views(503) PDF downloads(66) Cited by()

Proportional views

Image fusion algorithm based on DOG filter

1. Electronic and Optical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;

2. Qiqihar North Hua'an Group Test Site, Qiqihar 161000, China

Received Date: 2020-03-23

Rev Recd Date: 2020-06-13

Publish Date: 2020-09-22

Key words:

Abstract: Image fusion is one of the important contents in the field of image processing. The traditional fusion algorithm fuses the source images and processes them according to certain rules. Although a good fusion effect can be achieved, the algorithm requires high image registration, then the fusion image also has the problem of loss of details, and the problem that the target is not obvious enough. To improve the above problems, the characteristics of the infrared image, visible image and the infrared target were analyzed, target detection was used in image fusion, and the DOG filter was used to extract the targets in the infrared image. The fusion coefficient matrix was obtained through multi-scale DOG image calculation, and then the fusion sub-map was calculated. Finally, a fusion image with obvious targets and good details was obtained,and the requirement for image registration was reduced. Five commonly used evaluation indicators, as well as the signal-to-clutter ratio and background similarity, were used to evaluate the fusion image. Experimental results show that the proposed algorithm is superior to the commonly used image fusion methods in both subjective vision and objective evaluation indicators.

HTML

Reference (16)

[1]	Wang Guanglong, Tian Jie, Zhu Wenjie, et al. Feature fusion and weight adaptive updating based motion blur object tracking[J]. Optics and Precision Engineering,2019, 27(5):1158-1166. (in Chinese)王广龙, 田杰, 朱文杰, 等.特征融合和自适应权重更新相结合的运动模糊目标跟踪[J].光学精密工程, 2019, 27(5):1158-1166.
[2]	Wang Sheng. Dangerous objects detection in millimeter wave images[D]. Beijing:Tsinghua University, 2018. (in Chinese)王胜. 毫米波图像中危险物品检测[D]. 北京:清华大学, 2018.
[3]	Li Dezhi, Chen Hongda, Bi Feng, et al. Progress of multimodal molecular imaging technology in tumor diagnosis[J]. Chinese Journal of Analytical Chemistry, 2016, 44(10):1609-1618. (in Chinese)李德智, 陈宏达, 毕锋, 等.多模态分子影像技术在肿瘤诊断中的进展[J]. 分析化学, 2016, 44(10):1609-1618.
[4]	Shi Minhong. Summary of the development of image fusion technology[J]. Computer Era, 2019(9):27-29. (in Chinese)史敏红. 图像融合技术发展综述[J].计算机时代, 2019(9):27-29.
[5]	Rong Chuanzhen, Jia Yongxing, Yang Yu, et al. Image fusion based on infrared object extraction[J]. Journal of Signal Processing, 2019, 35(7):1141-1151. (in Chinese)荣传振, 贾永兴, 杨宇, 等.基于红外目标特征提取的图像融合方法[J]. 信号处理, 2019, 35(7):1141-1151.
[6]	Nan Tianzhang, Geng Jianjun, Chen Xu, et al. Infrared low, slow and small target detection based on neighborhood characteristics[J]. Infrared and Laser Engineering, 2019, 48(S1):S128002. (in Chinese)南天章, 耿建君, 陈旭, 等.基于邻域特征的红外低慢小目标检测[J]. 红外与激光工程, 2019, 48(S1):S128002.
[7]	Wu Yanfeng, Wang Yanjie, Sun Haijiang, et al. LSS-target detection in complex sky backgrounds[J]. Chinese Optics,2019, 12(4):854-866. (in Chinese)吴言枫, 王延杰, 孙海江, 等. 复杂动背景下的"低小慢"目标检测技术[J]. 中国光学, 2019, 12(4):854-866.
[8]	Lu Fuxing, Chen Xin, Chen Guilin, et al. Dim and small target detection based on background adaptive multi-feature fusion[J]. Infrared and Laser Engineering, 2019, 48(3):0326002. (in Chinese)陆福星, 陈忻, 陈桂林, 等.背景自适应的多特征融合的弱小目标检测[J]. 红外与激光工程, 2019, 48(3):0326002.
[9]	Lu Ruiqi, Ma Huimin. Template matching with multi-scale saliency[J]. Optics and Precision Engineering, 2018, 26(11):2776-2784. (in Chinese)逯睿琦, 马惠敏. 多尺度显著性区域提取的模板匹配[J]. 光学精密工程, 2018, 26(11):2776-2784.
[10]	Chen Lerui, Pan Qiuping, Kong Jinsheng. Research on image fusion based on saliency and guided filter[J]. Industrial Control Computer, 2016, 29(5):110-111. (in Chinese)陈乐瑞, 潘秋萍, 孔金生. 基于显著性和导向滤波的图像融合算法研究[J]. 工业控制计算机, 2016, 29(5):110-111.
[11]	Fu Zhizhong, Wang Xue, Li Xiaofeng, et al. Infrared and visible image fusion based on visual saliency and NSCT[J]. Journal of University of Electronic Science and Technology of China, 2017, 46(2):357-362. (in Chinese)傅志中, 王雪, 李晓峰, 等.基于视觉显著性和NSCT的红外与可见光图像融合[J]. 电子科技大学学报, 2017, 46(2):357-362.
[12]	Qiao Tieying, Cai Lihua, Li Ning, et al. Opposite target measurement based on infrared radiation characteristic system[J]. Chinese Optics, 2018, 11(5):804-811. (in Chinese)乔铁英, 蔡立华, 李宁, 等.基于红外辐射特性系统实现对面目标测量[J]. 中国光学, 2018, 11(5):804-811.
[13]	Cao Lin, Lu Hongying, Cheng Lin. Application of improved DOG operator in image edge extraction[J]. Computer Knowledge and Technology, 2018, 14(6):139-142. (in Chinese)曹麟, 鲁红英, 程琳.基于改进的DOG算子在图像边缘提取中的应用研究[J]. 电脑知识与技术, 2018, 14(6):139-142.
[14]	Birch P, Mitra B, Bangalore N M, et al. Approximate bandpass and frequency response models of the difference of Gaussian filter[J]. Optics Communications, 2010, 283(24):4942-4948.
[15]	Xia Mingge, Ouyang Wen. Pixel level image fusion methods and fusion performance evaluation[J]. Remote Sensing Technology and Application, 2002(4):224-229. (in Chinese)夏明革, 欧阳文. 像素级图像融合方法与融合效果评价[J]. 遥感技术与应用, 2002(4):224-229.
[16]	Yu Xianchuan, Pei Wenjing. Performance evaluation of image fusion quality metrics for the quality of different fusion methods[J]. Infrared and Laser Engineering, 2012, 41(12):3416-3422. (in Chinese)余先川, 裴文静.针对不同融合算法的质量评价指标性能评估[J]. 红外与激光工程, 2012, 41(12):3416-3422.

Image fusion algorithm based on DOG filter

doi: 10.3788/IRLA20200091

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views