The proposed JFDFusion method can stably preserve structural information and highlight key infrared targets across different scenes, providing high-quality inputs for subsequent visual perception tasks and achieves competitive subjective visual quality and objective evaluation performance.
Comparative experiments against eleven representative fusion methods on the MSRS and M3FD datasets show that SDC-Fusion ranks first in SSIM, VIF, Qabf, SF, and PSNR on MSRS, and first in SSIM, VIF, Qabf, SD, and PSNR on M3FD, while ranking second in the remaining two metrics on each dataset.
Xiao-Xia Wang, Shuang Guo, Fengbao Yang et al.· Italian National Conference...· 0 citations
A lightweight end-to-end IVIF network with two complementary refinement modules that achieves the best or tied-best value on three of seven standard fusion-quality metrics on FMB and four of seven on LLVIP, and ablation results further confirm the complementary effects of MSG and DGM.
Wenhua Zhao, Lei Zhong· Applied Sciences· 0 citations
A knowledge distillation-based training strategy, together with a structural consistency constraint, is introduced to enhance the fusion quality of the lightweight model by guiding the student network to inherit discriminative representations from the teacher model while preserving a compact architecture.
Wenkuan Xie, Weiguo Pan, Jian-Cheng Zhang et al.· International Conference on...· 0 citations
FAViT (Frequency-Aware Vision Transformer), a hybrid architecture capable of jointly utilizing spatial- and frequency-domain forensic information by the means of a bidirectional cross-attention fusion scheme, is presented.
Wasin Alkishri, Shahid Kamal, Jabar H. Yousif· Information· 0 citations
A novel spatial-frequency decoupled learning framework, termed spatial-frequency decoupled network (SFDNet), which decomposes features into low-frequency semantic structures and high-frequency detail components for independent representation learning and adaptive fusion is proposed.
Yufeng Li, Lei Yu, Chuan-Long Xie et al.· Engineering Research Express· 0 citations
LGF-Net is proposed, a unified framework that jointly models spatial semantics and adaptive spectral cues for deepfake detection and achieves competitive intra-dataset and cross-dataset performance compared with several state-of-the-art deepfake detection methods.
