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).

使用惯性测量单元数据，本文提出了一个简单而高效的框架来合成和恢复运动模糊图像，包括训练三元组的生成策略和用于恢复模糊图像的陀螺仪辅助运动去模糊网络。通过全面实验证明了该框架的优点：我们的合成和真实世界模糊轨迹之间仅有两个像素的误差，对于峰值信噪比，与最先进的去模糊方法 MIMO 相比，有明显的改善（约提高 33.17%）。

陀螺仪辅助运动去模糊网络

Gyroscope-Assisted Motion Deblurring Network

Coarse-to-fine strategies have been extensively used for the architecture
design of single image deblurring networks. Conventional methods typically
stack sub-networks with multi-scale input images and gradually improve
sharpness of images from the bottom sub-network to the top sub-network,
yielding inevitably high computational costs. Toward a fast and accurate
deblurring network design, we revisit the coarse-to-fine strategy and present a
multi-input multi-output U-net (MIMO-UNet). The MIMO-UNet has three distinct
features. First, the single encoder of the MIMO-UNet takes multi-scale input
images to ease the difficulty of training. Second, the single decoder of the
MIMO-UNet outputs multiple deblurred images with different scales to mimic
multi-cascaded U-nets using a single U-shaped network. Last, asymmetric feature
fusion is introduced to merge multi-scale features in an efficient manner.
Extensive experiments on the GoPro and RealBlur datasets demonstrate that the
proposed network outperforms the state-of-the-art methods in terms of both
accuracy and computational complexity. Source code is available for research
purposes at this https URL

提出了一种多输入多输出的 U-net（MIMO-UNet）模型，采用粗到细的策略进行单幅图像去模糊网络的架构设计，其中引入了不对称特征融合技术，经实验证明该模型在精度和计算复杂度方面均优于现有方法。