Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.
arXiv:2608. 05771v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) has achieved substantial progress under domain-consistent evaluation, yet detector performance often degrades markedly when generalizing to unseen infrared domains.
By Aohua Li, Jin Kuang, Yubing Lu, Pingping Liu
arXiv:2608.27971v1 Announce Type: new
Abstract: Unmanned aerial vehicle (UAV) multimodal perception integrates visible (RGB), infrared (IR), synthetic aperture radar (SAR), and depth sensors for scen...
By Jingpu Yang, Debin Tang, Yilin Sun, Fengxian Ji, Jiahua Zhu, Wenrui Ding, Yufeng Wang
Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception. While cross-sensor misalignment caused by different optics, viewpoints, fields of view, and exposure timings hinders practical deployment.
arXiv:2607. 24110v1 Announce Type: cross Abstract: Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception.
By Minchong Chen, Xiaoyun Yuan, Minyu Cao, Jianing Zhang, Jun Zhang, Shuyang Liu, Xiaokang Yang
The paper introduces the Learned Perceptual Image Fusion Measure (LPIFM), a model trained on dense human pairwise comparisons to assess infrared-visible image fusion. LPIFM jointly processes both source images and fused candidates using a shared hierarchical encoder, triadic interaction, and a tie-aware objective, achieving high agreement with human judgments and outperforming 19 conventional metrics. The authors release a large comparison corpus, model weights, and code, demonstrating LPIFM’s rapid adaptability to new fusion-evaluation protocols.
By Haoran Liu, Mingzhe Liu, Peng Li, Guibin Zan