  Image deblurring aims to remove undesired blurs from an image captured in a
dynamic scene. Much research has been dedicated to improving deblurring
performance through model architectural designs. However, there is little work
on data augmentation for image deblurring. Since continuous motion causes
blurred artifacts during image exposure, we aspire to develop a groundbreaking
blur augmentation method to generate diverse blurred images by simulating
motion trajectories in a continuous space. This paper proposes Implicit
Diffusion-based reBLurring AUgmentation (ID-Blau), utilizing a sharp image
paired with a controllable blur condition map to produce a corresponding
blurred image. We parameterize the blur patterns of a blurred image with their
orientations and magnitudes as a pixel-wise blur condition map to simulate
motion trajectories and implicitly represent them in a continuous space. By
sampling diverse blur conditions, ID-Blau can generate various blurred images
unseen in the training set. Experimental results demonstrate that ID-Blau can
produce realistic blurred images for training and thus significantly improve
performance for state-of-the-art deblurring models. The source code is
available at https://github.com/plusgood-steven/ID-Blau.
