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).

翻译：近年来，图像研究领域对去模糊网络的关注度显著提升。然而，由于缺乏像素对齐的训练三元组（背景图像、模糊图像和模糊热力图）以及模糊图像本身信息的局限性，这些网络在实际运动模糊场景中的实用价值仍十分有限。本文提出了一种简洁高效的框架，利用惯性测量单元（IMU）数据合成并还原运动模糊图像。值得注意的是，该框架包含用于生成训练三元组的策略，以及用于模糊图像复原的陀螺仪辅助运动去模糊（GAMD）网络。其核心原理在于：通过利用IMU数据，可确定图像曝光阶段相机姿态的变换，从而推导三维空间中每个点的运动轨迹（即模糊轨迹）。因此，采用该策略合成的三元组具有天然接近真实运动模糊、严格像素对齐且可批量生产的优势。通过全面实验验证，本框架展现出显著优越性：合成模糊轨迹与真实模糊轨迹之间仅存在两个像素的误差，同时将当前最先进的去模糊方法MIMO的峰值信噪比（PSNR）指标提升约33.17%。