Paper Publications
- [21] Yu Liu, Hao Zhao, Rencheng Song, Xudong Chen, Chang Li, Xun Chen, “SOM-Net: Unrolling the Subspace-based Optimization for Solving Full-wave Inverse Scattering Problems,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, p. 2007715, 2022.
- [22] Yu Liu, Fuhao Mu, Yu Shi, Xun Chen, “SF-Net: A Multi-task Model for Brain Tumor Segmentation in Multimodal MRI via Image Fusion,” IEEE Signal Processing Letters, vol. 29, pp. 1799-1803, 2022.
- [23] Yu Liu, Lei Wang, Huafeng Li, Xun Chen, “Multi-focus image fusion with deep residual learning and focus property detection,” Information Fusion, vol. 86-87, pp. 1-16, 2022.
- [24] Yu Liu, Yu Shi, Fuhao Mu, Juan Cheng, Chang Li, Xun Chen, “Multimodal MRI Volumetric Data Fusion with Convolutional Neural Networks,” IEEE Transactions on Instrumentation and Measurement, vol. 71, p. 4006015, 2022.
- [25] Yu Liu, Yu Shi, Fuhao Mu, Juan Cheng, Xun Chen, “Glioma Segmentation-Oriented Multi-modal MR Image Fusion with Adversarial Learning,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 8, pp. 1528-1531, 2022.
- [26] Wei Tang, Fazhi He, Yu Liu, Yansong Duan, “MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer,” IEEE Transactions on Image Processing, vol. 31, pp. 5134-5149, 2022. (ESI Highly Cited Paper)
- [27] Yu Liu, Lei Wang, Juan Cheng, Xun Chen, “Multiscale feature interactive network for multifocus image fusion,” IEEE Transactions on Instrumentation and Measurement, vol. 70, p. 5019316, 2021.
- [28] Rencheng Song, Qiao Zhou, Yu Liu*, Chang Li, Xun Chen, “A convolutional sparsity regularization for solving inverse scattering problems,” IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 12, pp. 2285-2289, 2021.
- [29] Wei Tang, Yu Liu*, Juan Cheng, Chang Li, Xun Chen, “Green fluorescent protein and phase contrast image fusion via detail preserving cross network,” IEEE Transactions on Computational Imaging, vol. 7, pp. 584-597, 2021.
- [30] Huafeng Li, Yueliang Cen, Yu Liu*, Xun Chen, Zhengtao Yu, “Different input resolutions and arbitrary output resolution: A meta learning-based deep framework for infrared and visible image fusion,” IEEE Transactions on Image Processing, vol. 30, pp. 4070-4083, 2021.
MOBILE Version