  Image research has shown substantial attention in deblurring networks in
recent years. Yet, their practical usage in real-world deblurring, especially
motion blur, remains limited due to the lack of pixel-aligned training triplets
(background, blurred image, and blur heat map) and restricted information
inherent in blurred images. This paper presents a simple yet efficient
framework to synthetic and restore motion blur images using Inertial
Measurement Unit (IMU) data. Notably, the framework includes a strategy for
training triplet generation, and a Gyroscope-Aided Motion Deblurring (GAMD)
network for blurred image restoration. The rationale is that through harnessing
IMU data, we can determine the transformation of the camera pose during the
image exposure phase, facilitating the deduction of the motion trajectory (aka.
blur trajectory) for each point inside the three-dimensional space. Thus, the
synthetic triplets using our strategy are inherently close to natural motion
blur, strictly pixel-aligned, and mass-producible. Through comprehensive
experiments, we demonstrate the advantages of the proposed framework: only
two-pixel errors between our synthetic and real-world blur trajectories, a
marked improvement (around 33.17%) of the state-of-the-art deblurring method
MIMO on Peak Signal-to-Noise Ratio (PSNR).
