Infrared and visible image fusion based on edge-preserving and attention generative adversarial network

Wen-Qing ZHU; Xin-Yi TANG; Rui ZHANG; Xiao CHEN; Zhuang MIAO

doi:10.11972/j.issn.1001-9014.2021.05.017

[1] J Y Ma, Y Ma, C Li. Infrared and visible image fusion methods and applications: A survey. Information Fusion, 45, 153-178(2019).

[2] G X Liu, W H Yang. Image fusion scheme of pixel-level and multi-operator for infrared and visible light images. J. Infrared Millim. Waves, 20, 207-210(2001).

[3] K P Upla, M V Joshi, P P Gajjar. An Edge Preserving Multiresolution Fusion: Use of Contourlet Transform and MRF Prior. IEEE Transactions on Geoscience and Remote Sensing, 53, 3210-3220(2015).

[4] S P Liu, Y Fang. Infrared image fusion algorithm based on contourlet transform and improved pulse coupled neural network. J. Infrared Millim. Waves, 26, 217-221(2007).

[5] S Li, X Kang, J Hu. Image fusion with guided filtering. IEEE Transactions on Image Processing, 22, 2864-2875(2013).

[6] Y Liu, Z F Wang. Simultaneous image fusion and denoising with adaptive sparse representation. Image Processing Iet, 9, 347-357(2015).

[7] S T Li, J T Kwok, Y N Wang. Multifocus image fusion using artificial neural networks. Pattern Recognition Letters, 23, 985-997(2002).

[8] Y Liu, X Chen, J Cheng et al. Infrared and visible image fusion with convolutional neural networks. International Journal of Wavelets Multiresolution and Information Processing, 16(2018).

[9] H Li, X J Wu, J Kittler. Infrared and Visible Image Fusion using a Deep Learning Framework. 2018 24th International Conference on Pattern Recognition (ICPR), 2705-2710(2018).

[10] H Li, X J Wu. DenseFuse: A Fusion Approach to Infrared and Visible Images. IEEE Transactions on Image Processing, 2614-2623(2018).

[11] J Y Ma, W Yu, P W Liang et al. FusionGAN: A generative adversarial network for infrared and visible image fusion. Information Fusion, 48, 11-26(2019).

[12] J T Xu, X P Shi, S Z Qin et al. LBP-BEGAN: A generative adversarial network architecture for infrared and visible image fusion. Infrared Physics & Technology, 104(2020).

[13] Q Li, L Lu, Z Li et al. Coupled GAN with Relativistic Discriminators for Infrared and Visible Images Fusion. IEEE Sensors Journal, 1-1(2019).

[14] I J Goodfellow, J Pouget-Abadie, M Mirza et al. Generative Adversarial Nets. Advances in Neural Information Processing Systems 27 (NIPS2014), 27(2014).

[15] X D Mao, Q Li, H R Xie et al. Least Squares Generative Adversarial Networks. 2017 IEE International Conference on Computer Vision (ICCV), 2813-2821(2017).

[16] K Simonyan, A J C e Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. 2014 International Conference on Learning Representations(2014).

[17] Y Zhang, K Li, K Li et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks. 2018 European Conference on Computer Vision, 294-310(2018).

[18] S Zagoruyko, N Komodakis.. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. International Conference on Learning Representations(2017).

[19] V Nair, E H Geoffrey. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, 807-814(2010).

[20] F Wang, M Q Jiang, C Qian et al. Residual Attention Network for Image Classification. 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2017), 6450-6458(2017).

[21] J Johnson, A Alahi, F F Li. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. European Conference on Computer Vision, 694-711(2016).

[22] H Xu, J Ma, J Jiang et al. U2Fusion: A Unified Unsupervised Image Fusion Network. IEEE Transactions on Pattern Analysis and Machine Intelligence(2020).

[23] D P Kingma, J Ba. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations(2014).

[24] J Y Ma, C Chen, C Li et al. Infrared and visible image fusion via gradient transfer and total variation minimization. Information Fusion, 31, 100-109(2016).

[25] J W Roberts, J van Aardt, F Ahmed. Assessment of image fusion procedures using entropy, image quality, and multispectral classification. Journal of Applied Remote Sensing, 2(2008).

[26] V Aslantas, E Bendes. A new image quality metric for image fusion: The sum of the correlations of differences. Aeu-International Journal of Electronics and Communications, 69, 160-166(2015).

[27] A M Eskicioglu, P S Fisher. Image quality measures and their performance. IEEE Transactions on Communications, 43, 2959-2965(1995).

[28] B Rajalingam, R Priya. Hybrid Multimodality Medical Image Fusion Technique for Feature Enhancement in Medical Diagnosis. International Journal of Engineering Science Invention(2018).