arXiv:2608.21786v2 Announce Type: replace
Abstract: General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific mode...
By Xingxin Xu, Siqi Zhao, Xin Li, Xinjie Yao, Yiming Sun, Pengfei Zhu
arXiv:2609.31620v1 Announce Type: new
Abstract: Representation autoencoders (RAEs) reuse features from a pretrained visual encoder as reconstruction and diffusion latents, integrating strong visual r...
By Hongyang Du, Yunfei Xie, Junjie Ye, Jiawei Yang, Xiaoyan Cong, Haodong Zhang, Yongchao Huang, Haiyu Wu, Zongxia Li, Shihang Gui, Dawei Liu, Runhao Li, Jingcheng Ni, Chen Wei, Randall Balestriero, Yue Wang
arXiv:2608. 20141v1 Announce Type: new Abstract: All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model.
By Zhaokun He, Kangbiao Shi, Axi Niu, Jian Jin, Peng Wu, Wei Dong, Qingsen Yan
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.
The paper introduces W2F, a visual‑semantic mutual‑reinforcing framework for multi‑modal image fusion (MMIF). It enhances feature complementarity with a segmentation‑guided fusion loss and an embedded information restoration prior, and employs interactive gated mixed attention (IGM‑Att) and progressive coupling attention (PC‑Att) to fuse local and global information. A collaborative mechanism with a feature search module and gradient correction further aligns visual and semantic fusion, leading to state‑of‑the‑art visual appeal and decision accuracy.
By Hao Zhang, Xuhui Zuo, Jiayi Ma
The paper introduces IT-TextFusion, an iterative text-guided image fusion framework that uses text-conditioned feature interaction across multiple fusion and refinement stages. It incorporates deep cross-attention, multi-scale cross-gate fusion, and stage-specific text-conditioned modulation to enable degradation-aware global semantic conditioning while preserving complementary visible and infrared information. Experiments on benchmark datasets demonstrate improvements in information-preservation and perceptual-quality metrics, with some metric-dependent trade-offs.
By Siyang Liu, Peiyi Zhou, Tianle Jin, Rongrong Bian, Zheke Jin, Mengze Gao