TY - JOUR
T1 - Adaptive Reliability-Calibrated Consensus–Complementarity–Conflict Modeling for Infrared and Visible Image Fusion
AU - Tian, Bowen
AU - Luo, Jihao
AU - Lin, Ke
AU - Zhang, Changqing
AU - Qin, Tong
N1 - Publisher Copyright:
© 2026 by the authors.
PY - 2026/8
Y1 - 2026/8
N2 - Infrared and visible image fusion needs to preserve visible texture details and infrared thermal saliency, yet emphasizing one modality may suppress or distort useful information from the other, while cross-modal differences may also contain noise, pseudo-textures, or locally incompatible boundaries. We propose ARC3Fusion, which reformulates image fusion as a reliability-calibrated consensus–complementarity–conflict process to achieve a more effective balance between visible texture detail and infrared target saliency. A progressive shared encoder and a modality-specific residual adapter first produce comparable yet modality-aware features. Cross-Modal Explainable Residual Decomposition then estimates jointly supported consensus and represents the information unexplained by the opposite modality as candidate residuals. Trustworthy Complementarity Verification evaluates infrared residuals using source intensity and edge evidence, while visible residuals are examined using source cues and learnable frequency-pattern evidence. Cross-Modal Conflict Estimation further characterizes local incompatibility through co-activation, reliability, amplitude imbalance, edge-strength mismatch, and orientation mismatch. Conflict-Aware Routing finally coordinates consensus and verified residuals according to these relation cues. Unlike conventional shared–private decomposition that directly preserves private features, ARC3Fusion treats modality-specific residuals as candidates that must be verified and conflict-coordinated before fusion. Experiments on LLVIP, MSRS, and TNO demonstrate consistent fusion performance. On LLVIP, ARC3Fusion achieves the best EN, SF, AG, VIF, and SCD values of 7.158, 14.467, 4.331, 1.136, and 1.229, respectively. These results indicate that verifying modality-specific residuals and coordinating local conflicts improves the joint preservation of visible texture details and infrared thermal saliency.
AB - Infrared and visible image fusion needs to preserve visible texture details and infrared thermal saliency, yet emphasizing one modality may suppress or distort useful information from the other, while cross-modal differences may also contain noise, pseudo-textures, or locally incompatible boundaries. We propose ARC3Fusion, which reformulates image fusion as a reliability-calibrated consensus–complementarity–conflict process to achieve a more effective balance between visible texture detail and infrared target saliency. A progressive shared encoder and a modality-specific residual adapter first produce comparable yet modality-aware features. Cross-Modal Explainable Residual Decomposition then estimates jointly supported consensus and represents the information unexplained by the opposite modality as candidate residuals. Trustworthy Complementarity Verification evaluates infrared residuals using source intensity and edge evidence, while visible residuals are examined using source cues and learnable frequency-pattern evidence. Cross-Modal Conflict Estimation further characterizes local incompatibility through co-activation, reliability, amplitude imbalance, edge-strength mismatch, and orientation mismatch. Conflict-Aware Routing finally coordinates consensus and verified residuals according to these relation cues. Unlike conventional shared–private decomposition that directly preserves private features, ARC3Fusion treats modality-specific residuals as candidates that must be verified and conflict-coordinated before fusion. Experiments on LLVIP, MSRS, and TNO demonstrate consistent fusion performance. On LLVIP, ARC3Fusion achieves the best EN, SF, AG, VIF, and SCD values of 7.158, 14.467, 4.331, 1.136, and 1.229, respectively. These results indicate that verifying modality-specific residuals and coordinating local conflicts improves the joint preservation of visible texture details and infrared thermal saliency.
KW - complex-condition perception
KW - conflict-aware routing
KW - infrared and visible image fusion
KW - multimodal remote sensing
KW - trustworthy complementarity verification
UR - https://www.scopus.com/pages/publications/105046990110
U2 - 10.3390/s26154745
DO - 10.3390/s26154745
M3 - Article
C2 - 42590522
AN - SCOPUS:105046990110
SN - 1424-8220
VL - 26
JO - Sensors
JF - Sensors
IS - 15
M1 - 4745
ER -