T${}^3$Fusion: Test-Time Training for Task-General Image Fusion

Image fusion combines complementary observations into a more informative representation for subsequent perception and analysis. However, existing deep fusion models often generalize poorly to unseen datasets and tasks because fusion rules learned offline may not capture the relationships between sources in new inputs. To address this limitation, we propose T${}^3$Fusion, a test-time training framework that adapts fusion by learning cross-source relations from the current input pair during inference. Specifically, a fusion network extracts multiscale features from the two source images. We fit compact bidirectional mappings between these features through closed-form ridge regression so that each source predicts the other's features. A learned fusion readout combines these predictions and their residuals with the original features to refine the representations passed to the decoder for image reconstruction. The network weights remain fixed while the mappings are fitted to each test pair without fused ground truth or test-time backpropagation. Extensive experiments across five fusion tasks show that a single MSRS-trained model generalizes to unseen datasets and tasks without task-specific fine-tuning, achieving better fusion quality and downstream task performance.