| Citation: |
YANG Yanchun, LEI Huiyun, YANG Wanxuan. Infrared and Visible Image Fusion Based on Fast Joint Bilateral Filtering and Improved PCNN[J]. Infrared Technology , 2024, 46(8): 892-901.
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To address the problems of detail loss, inconspicuous targets, and low contrast in infrared and visible image fusion, a fusion method combining fast joint bilateral filtering (FJBF) and an improved pulse-coupled neural network (PCNN) was proposed. The operational efficiency can be effectively improved by ensuring the quality of the fused image. First, the source images were decomposed by fast joint bilateral filtering. Second, to extract significant structure and target information, a weighted average fusion rule based on a visual saliency graph (VSM) was adopted for the basic layer image, and an improved pulse-coupled neural network model was adopted for the detail layer image. All parameters of the PCNN can be adjusted according to the input bands, and the fusion image was reconstructed using the superimposed fusion map of the base layer and the fusion map of the detail layer. The experimental results show that this method can significantly improve the image fusion effect and effectively retain important information, such as targets, background details, and edges.
| [1] |
MA J Y, ZHANG H, SHAO Z F, et al. GANMC: A generative adversarial network with multi classification constraints for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation, 2021, 70: 5005014.
|
| [2] |
蒋杰伟, 刘尚辉, 金库, 等. 基于FCM与引导滤波的红外与可见光图像融合[J]. 红外技术, 2023, 45(3): 249-256. http://hwjs.nvir.cn/article/id/67d60996-565d-4597-96a1-937255cc33cc
JIANG J W, LIU S H, JIN K, et al. Infrared and visible-light image fusion based on FCM and guided filtering [J]. Infrared Technology, 2023, 45(3): 249-256. http://hwjs.nvir.cn/article/id/67d60996-565d-4597-96a1-937255cc33cc
|
| [3] |
ZHANG Z Y, ZHANG Y M, CAO Y F, et al. Infrared and visible airborne targets image fusion with applications to sense and avoid[J]. IFAC Papers On Line, 2020, 53(2): 14742-14747. DOI: 10.1016/j.ifacol.2020.12.1892
|
| [4] |
MA J Y, MA Y, LI C. Infrared and visible image fusion methods and applications: A survey[J]. Information Fusion, 2019, 45: 153-178. DOI: 10.1016/j.inffus.2018.02.004
|
| [5] |
JIN H Y, WANG Y Y. A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization[J]. Infrared Physics & Technology, 2014, 64: 134-142.
|
| [6] |
LIU X H, CHEN Z B, QIN M Z. Infrared and visible image fusion using guided filter and convolutional sparse representation[J]. Opt. Precision Eng, 2018, 26(5): 1242-1253. DOI: 10.3788/OPE.20182605.1242
|
| [7] |
林剑萍, 廖一鹏. 结合分数阶显著性检测及量子烟花算法的NSST域图像融合[J]. 光学 精密工程, 2021, 29(6): 1406-1419. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM202106021.htm
LIN J P, LIAO Y P. NSST region image fusion based on fractional significance detection and quantum fireworks algorithm[J]. Optics and Precision Engineering, 2021, 29(6): 1406-1419. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM202106021.htm
|
| [8] |
LIU Y, MIAO C Y, JI J H, et al. MMF: a multi scale MobileNet based fusion method for infrared and visible image[J]. Infrared Physics & Technology, 2021, 119: 103894.
|
| [9] |
陈彦林, 王志社, 邵文禹, 等. 红外与可见光图像多尺度Transformer融合方法[J]. 红外技术, 2023, 45(3): 266-275. http://hwjs.nvir.cn/article/id/8d183327-f396-4c96-b8c4-24ab8acb6a44
CHEN Y L, WANG Z S, SHAO W Y, et al. Multi-scale transformer fusion method for infrared and visible images[J]. Infrared Technology, 2023, 45(3): 266-275. http://hwjs.nvir.cn/article/id/8d183327-f396-4c96-b8c4-24ab8acb6a44
|
| [10] |
LI S T, KANG X D, HU J W. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2864-2875. DOI: 10.1109/TIP.2013.2244222
|
| [11] |
ZHAN K, KONG L G, LIU B, et al. Multimodal image seamless fusion[J]. Journal of electronic imaging, 2019, 28(2): 023027.
|
| [12] |
JIAN L H, YANG X M, ZHOU Z L, et al. Multi scale image Fusion through rolling guidance filter[J]. Future Generation Computer Systems, 2018, 83(6): 310-325.
|
| [13] |
KONG W W, CHEN Y P, LEI Y. Medical image fusion using guided filter random walks and spatial frequency in framelet domain[J]. Signal Processing, 2021, 181: 107921. DOI: 10.1016/j.sigpro.2020.107921
|
| [14] |
FENG Y C, WU J, HU X H, et al. Medical image fusion using bilateral texture filtering[J]. Biomedical Signal Processing and Control, 2023, 85: 105004. DOI: 10.1016/j.bspc.2023.105004
|
| [15] |
ZHANG Y G, ZHAO P, MA Y Z, et al. Multi-focus image fusion with joint guided image filtering[J]. Signal Processing: Image Communication, 2021, 92: 35-46.
|
| [16] |
成飞飞, 付志涛, 黄亮, 等. 结合自适应PCNN的非下采样剪切波遥感影像融合[J]. 测绘学报, 2021, 50(10): 1380-1389. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202110012.htm
CHENG F F, FU Z T, HUANG L, et al. Non-subsampled shearlet wave remote sensing image fusion combined with adaptive PCNN [J]. Chinese Journal of Surveying and Mapping, 2021, 50(10): 1380-1389. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202110012.htm
|
| [17] |
李向阳, 曹宇彤, 陈笑, 等. 基于自适应NSST-PCNN的红外与可见光图像融合方法研究[J]. 长春理工大学学报: 自然科学版, 2021, 44(5): 12-18. https://cdmd.cnki.com.cn/Article/CDMD-10110-1024330949.htm
LI X Y, CAO Y T, CHEN X, et al. Research on infrared and visible image fusion method based on adaptive NSST-PCNN[J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2021, 44(5): 12-18. https://cdmd.cnki.com.cn/Article/CDMD-10110-1024330949.htm
|
| [18] |
TOET A. TNO image fusion dataset[DB/OL]. (2014). https//figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029.
|
| [19] |
LIU Y, CHEN X, RABAB K, et al. Medical image fusion via convolutional sparsity based morphological component analysis[J]. IEEE Signal Processing Letters, 2019, 26(3): 485-489.
|
| [20] |
ZHANG Y, ZHANG L J, BAI X Z, et al. Infrared and visual image fusion through infrared feature extraction and visual information preservation[J]. Infrared Physics & Technology, 2017, 83: 227-237. http://www.sciencedirect.com/science/article/pii/S1350449517300725
|
| [21] |
MA J L, ZHOU Z Q, WANG B, et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 2017, 82: 8-13.
|
| [22] |
LIU F, SHEN T S, MA X X. Image fusion of cross bilateral filtering and visual weight information[J]. Chinese Journal of Scientific Instrument, 2017, 38(4): 1005-1013.
|
| [23] |
TANG L F, YUAN J T, ZHANG H, et al. PIA fusion: a progressive infrared and visible image fusion network based on illumination aware[J]. Information Fusion, 2022, 83: 79-92.
|
| [24] |
LI H, WU X J, JOSEF K. RFN-Nest: An end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 2021, 73: 72-86.
|
| [25] |
ZHANG H, MA J Y. SDNet: A versatile squeeze and decomposition network for real-time image fusion[J]. International Journal of Computer Vision, 2021, 129: 2761-2785.
|
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