Image registration and fusion aim to establish spatial correspondences from misaligned multi-modal source images, and integrate complementary information. However, in real-world imaging scenarios, source images are often affected by complex and diverse degradations, such as low illumination, noise, etc., which severely...
Xun-Peng Yi, Zai-Xi Du, Qing-Long Yan et al.· 0 citations
Existing multi-modal image fusion (MMIF) methods generally face an optimization gap between the visual and semantic domains, limiting the enhancement of perception capabilities. To overcome this long-standing barrier, this work proposes W2F, a new visual-semantic mutual reinforcing paradigm for MMIF. First, we reinforc...
SpeciFuse, an infrared-visible image fusion network that learns degradation-specific representations that effectively addresses the conflicts arising from multiple degradations, thereby achieving robust fusion performance that generalizes to diverse composite degradation scenarios.
Xuan Li, Zhao-Ming Feng, Xiang-Han Yuan et al.· Proceedings of the Thirty-Fi...· 0 citations
DuS-DiFuse is proposed, a robust dual-stream latent diffusion framework composed of a diffusion fusion unit and a generative modulation unit that achieves leading fusion performance, exhibits strong robustness to heterogeneous degradations, generalizes well across fusion tasks, and supports effective controllable gener...
Lei Cao, Hao Zhang, Peng Zhang et al.· IEEE Transactions on Pattern...· 0 citations
An innovative degradation-aware image fusion framework via leveraging text-driven semantics, termed GText-IF, which can perform effective degradation-aware fusion in real degraded image scenarios, exhibiting remarkable advancement, robustness, and practicality.
Xun-Peng Yi, Han Xu, Yi-Bing Zhang et al.· IEEE Transactions on Pattern...· 1 citation
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