TY  - JOUR
AU  - Meng, Yanchen
AU  - Yang, Fengbao
AU  - Ji, Linna
AU  - Wang, Xiaoxia
AU  - Guo, Xiaoming
PY  - 2026
DA  - 2026/04/17
TI  - Salient Feature-Driven Bimodal Video Mimic Fusion Algorithm
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 3
IS  - 2
SP  - 74
EP  - 92
DO  - 10.62762/CJIF.2025.874404
UR  - https://www.icck.org/article/abs/CJIF.2025.874404
KW  - mimic fusion
KW  - video fusion
KW  - infrared and visible image
KW  - possibility theory
AB  - In complex dynamic environments, infrared and visible video sequences exhibit highly variable and unpredictable feature distributions. Existing fusion algorithms with fixed architectures cannot adaptively respond to these dynamic feature changes, resulting in blurred fusion outcomes and the loss of critical detail information. To address this limitation, we propose a salient feature-driven mimic fusion algorithm that continuously monitors feature variations and dynamically reconfigures the fusion architecture to maintain optimized fusion performance. First, we extract amplitude and frequency attributes from infrared and visible video features and perform weighted fusion to calculate single-modality temporal features and cross-modal intra-frame difference features. Second, based on clustering statistical properties of feature distributions, we construct possibility distribution functions to quantify the degree of feature variation, design synthesis rules to derive comprehensive possibility values of feature change, and utilize significantly changing features as driving factors for subsequent mimic variant adjustments. Building upon this foundation, we establish a fusion validity evaluation function by analyzing correlation coefficients between feature changes and fusion quality metrics, and accordingly construct mapping relationships between features and mimic variants. Finally, we determine the optimal mimic variant combination by synthesizing various feature change characteristics to implement mimic fusion. Experimental evaluation demonstrates that our proposed method significantly outperforms existing approaches in adaptive fusion performance in dynamic scenes, with superior preservation of edge and texture details in the fusion results.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Meng2026Salient,
  author = {Yanchen Meng and Fengbao Yang and Linna Ji and Xiaoxia Wang and Xiaoming Guo},
  title = {Salient Feature-Driven Bimodal Video Mimic Fusion Algorithm},
  journal = {Chinese Journal of Information Fusion},
  year = {2026},
  volume = {3},
  number = {2},
  pages = {74-92},
  doi = {10.62762/CJIF.2025.874404},
  url = {https://www.icck.org/article/abs/CJIF.2025.874404},
  abstract = {In complex dynamic environments, infrared and visible video sequences exhibit highly variable and unpredictable feature distributions. Existing fusion algorithms with fixed architectures cannot adaptively respond to these dynamic feature changes, resulting in blurred fusion outcomes and the loss of critical detail information. To address this limitation, we propose a salient feature-driven mimic fusion algorithm that continuously monitors feature variations and dynamically reconfigures the fusion architecture to maintain optimized fusion performance. First, we extract amplitude and frequency attributes from infrared and visible video features and perform weighted fusion to calculate single-modality temporal features and cross-modal intra-frame difference features. Second, based on clustering statistical properties of feature distributions, we construct possibility distribution functions to quantify the degree of feature variation, design synthesis rules to derive comprehensive possibility values of feature change, and utilize significantly changing features as driving factors for subsequent mimic variant adjustments. Building upon this foundation, we establish a fusion validity evaluation function by analyzing correlation coefficients between feature changes and fusion quality metrics, and accordingly construct mapping relationships between features and mimic variants. Finally, we determine the optimal mimic variant combination by synthesizing various feature change characteristics to implement mimic fusion. Experimental evaluation demonstrates that our proposed method significantly outperforms existing approaches in adaptive fusion performance in dynamic scenes, with superior preservation of edge and texture details in the fusion results.},
  keywords = {mimic fusion, video fusion, infrared and visible image, possibility theory},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}