Trajectory-aware Differentiable Blur Augmentation for Image Deblurring

Image deblurring aims to recover sharp images degraded by motion blur caused by camera shake and object motion. Since collecting blur-sharp pairs covering diverse motion patterns is costly, blur augmentation provides an effective way to expand the training distribution, particularly under limited training data. However, existing methods primarily diversify blur patterns through random perturbations of blur magnitude and orientation, resulting in limited exploration of diverse motion trajectories underlying real-world blur formation. To overcome this limitation, we propose a differentiable trajectory-aware blur augmentation framework that explicitly expands motion-space coverage through trajectory modeling. Specifically, we introduce a Global Trajectory-aware Augmentation (GTA) module that parameterizes camera motion using motion dynamics and trajectory primitives to synthesize diverse motion trajectories. Furthermore, we propose a Local Trajectory-aware Augmentation (LTA) module to model spatially varying local motion, thereby capturing diverse local blur variations and enriching blur diversity. The differentiable formulation enables trajectory augmentation to be optimized through the deblurring objective. Experimental results demonstrate improved blur diversity and deblurring performance across both standard and limited-data training settings.