Reg-TTR, Test-Time Refinement for Fast, Robust and Accurate Image Registration
This work addresses the problem of domain shift and accuracy limitations in image registration for medical or computer vision applications, offering an incremental improvement to enhance foundation models.
The paper tackles the trade-off between speed and accuracy in image registration by proposing Reg-TTR, a test-time refinement framework that combines deep learning and conventional methods, achieving state-of-the-art performance with only a 21% increase in inference time (0.56s).
Traditional image registration methods are robust but slow due to their iterative nature. While deep learning has accelerated inference, it often struggles with domain shifts. Emerging registration foundation models offer a balance of speed and robustness, yet typically cannot match the peak accuracy of specialized models trained on specific datasets. To mitigate this limitation, we propose Reg-TTR, a test-time refinement framework that synergizes the complementary strengths of both deep learning and conventional registration techniques. By refining the predictions of pre-trained models at inference, our method delivers significantly improved registration accuracy at a modest computational cost, requiring only 21% additional inference time (0.56s). We evaluate Reg-TTR on two distinct tasks and show that it achieves state-of-the-art (SOTA) performance while maintaining inference speeds close to previous deep learning methods. As foundation models continue to emerge, our framework offers an efficient strategy to narrow the performance gap between registration foundation models and SOTA methods trained on specialized datasets. The source code will be publicly available following the acceptance of this work.