In this paper, we present GyroDeblurNet, a novel single image deblurring
method that utilizes a gyro sensor to effectively resolve the ill-posedness of
image deblurring. The gyro sensor provides valuable information about camera
motion during exposure time that can significantly improve deblurring quality.
However, effectively exploiting real-world gyro data is challenging due to
significant errors from various sources including sensor noise, the disparity
between the positions of a camera module and a gyro sensor, the absence of
translational motion information, and moving objects whose motions cannot be
captured by a gyro sensor. To handle gyro error, GyroDeblurNet is equipped with
two novel neural network blocks: a gyro refinement block and a gyro deblurring
block. The gyro refinement block refines the error-ridden gyro data using the
blur information from the input image. On the other hand, the gyro deblurring
block removes blur from the input image using the refined gyro data and further
compensates for gyro error by leveraging the blur information from the input
image. For training a neural network with erroneous gyro data, we propose a
training strategy based on the curriculum learning. We also introduce a novel
gyro data embedding scheme to represent real-world intricate camera shakes.
Finally, we present a synthetic dataset and a real dataset for the training and
evaluation of gyro-based single image deblurring. Our experiments demonstrate
that our approach achieves state-of-the-art deblurring quality by effectively
utilizing erroneous gyro data.

本研究通过利用陀螺传感器解决图像去模糊问题，提出了一种名为 GyroDeblurNet 的新型单幅图像去模糊方法。该方法有效利用陀螺传感器提供的有关曝光期间相机运动的宝贵信息，以显著提高去模糊质量。通过引入陀螺细化模块和陀螺去模糊模块对陀螺误差进行处理，并通过模糊信息进一步补偿陀螺误差，我们的方法能够有效利用陀螺数据实现最先进的去模糊效果。