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Image deblurring aims to remove undesired blurs from an image captured in adynamic scene. Much research has been dedicated to improving deblurringperformance through model architectural designs. However, there is little workon data augmentation for image deblurring. Since continuous motion causesblurred artifacts during image exposure, we aspire to develop a groundbreakingblur augmentation method to generate diverse blurred images by simulatingmotion trajectories in a continuous space. This paper proposes ImplicitDiffusion-based reBLurring AUgmentation (ID-Blau), utilizing a sharp imagepaired with a controllable blur condition map to produce a correspondingblurred image. We parameterize the blur patterns of a blurred image with theirorientations and magnitudes as a pixel-wise blur condition map to simulatemotion trajectories and implicitly represent them in a continuous space. Bysampling diverse blur conditions, ID-Blau can generate various blurred imagesunseen in the training set. Experimental results demonstrate that ID-Blau canproduce realistic blurred images for training and thus significantly improveperformance for state-of-the-art deblurring models.

ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation