IMAGE FUSION AND IMAGE QUALITY ASSESSMENT OF FUSED IMAGES

11 citations as recorded by crossref.

1. Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications A. Allu & S. Mesapam https://doi.org/10.1080/14498596.2024.2353158
2. SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis H. Xiang et al. https://doi.org/10.1109/JBHI.2025.3649749
3. Quality assessment of ten fusion techniques applied on Worldview-2 K. Nikolakopoulos & D. Oikonomidis https://doi.org/10.5721/EuJRS20154809
4. Impact of remote sensing data fusion on agriculture applications: A review A. Allu & S. Mesapam https://doi.org/10.1016/j.eja.2024.127478
5. DFPGAN: Dual fusion path generative adversarial network for infrared and visible image fusion S. Yi et al. https://doi.org/10.1016/j.infrared.2021.103947
6. CP-FN: A Collaborative Perception Fusion Network for Infrared and Visible Image Fusion W. Jiang et al. https://doi.org/10.1109/ACCESS.2025.3609880
7. Unsupervised Fusion of Misaligned PAT and MRI Images via Mutually Reinforcing Cross-Modality Image Generation and Registration Y. Zhong et al. https://doi.org/10.1109/TMI.2023.3347511
8. MMIF-INet: Multimodal medical image fusion by invertible network D. He et al. https://doi.org/10.1016/j.inffus.2024.102666
9. MMVI-Fusion: An interactive dual-encoder network with cross-task guidance for maritime visible–infrared fusion Y. Yin et al. https://doi.org/10.1016/j.infrared.2026.106506
10. A Review of Deep Learning Methods for Multimodal Medical Image Fusion Y. Shi & M. Rigou https://doi.org/10.3390/s26144632
11. SDFusion: Fractional-order structural tensor-guided dynamic frequency enhancement network for medical image fusion Q. Yu & Y. Wang https://doi.org/10.1016/j.bspc.2026.109682