IMD-Blur: Implicit Motion and Defocus Blur Augmentation for Image Deblurring

The scarcity of paired sharp/blur training data for image deblurring makes realistic blur synthesis important for data augmentation and model generalization. However, existing blur augmentation methods typically focus on isolated blur types, such as defocus or motion blur, and often rely on auxiliary signals, including optical flow, aperture settings, or depth/defocus maps, which are rarely available in practice. To address these limitations, we propose IMD-Blur (Implicit Motion and Defocus Blur Augmentation), a unified framework that learns pixel-wise blur parameters directly from paired sharp/blur images without external signals. Specifically, using a parameterized kernel-based blurring pipeline and a blur parameter estimator, IMD-Blur constructs a continuous and interpretable parameter space for observed blur patterns. Based on the estimated blur parameters, we design a diffusion-based blur generator that produces diverse and controllable motion- and defocus-blurred images, thereby densifying the observed blur distribution and improving training-distribution coverage. Experiments show that IMD-Blur produces realistic blurred images and consistently improves state-of-the-art deblurring models across multiple benchmarks.