Image blur caused by camera movement is common in long-exposure photography. A recent approach to address image blur is to record camera motion via inertial sensors in imaging equipment such as smartphones and single-lens reflex (SLR) cameras. However, because of device performance limitations, directly estimating a blur kernel from sensor data is infeasible. Previous works that have attempted to correct blurry image content via sensor data have also been susceptible to theoretical defects. Here, we propose a novel method of deblurring images that uses inertial sensors and a short-long-short (SLS) exposure strategy. Assisted short-exposure images captured before and after the formal long-exposure image are employed to correct the sensor data. A half-blind deconvolution algorithm is proposed to refine the estimated kernel. An extra smoothing filter is integrated into the framework to address the coarse initial kernel. Hence, we propose a fast solution for optimization that uses the iteratively reweighted least squares (IRLS) method in the frequency domain. We evaluate these methods via several blind deconvolutions. Quantitative indicators and the visual performance of the image deblurring results show that our method performs better than previous methods in terms of image quality restoration and computational time cost. This method will increase the feasibility of applying deblurring to imaging devices.

中文翻译：

---

利用惯性传感器和短-长-短曝光策略进行图像去模糊


相机移动引起的图像模糊在长时间曝光摄影中很常见。解决图像模糊问题的最新方法是通过智能手机和单镜头反光 (SLR) 相机等成像设备中的惯性传感器记录相机运动。然而，由于设备性能的限制，直接从传感器数据估计模糊内核是不可行的。之前尝试通过传感器数据校正模糊图像内容的工作也容易受到理论缺陷的影响。在这里，我们提出了一种使用惯性传感器和短-长-短（SLS）曝光策略的图像去模糊新方法。采用在正式长曝光图像之前和之后捕获的辅助短曝光图像来校正传感器数据。提出了半盲反卷积算法来细化估计的内核。框架中集成了一个额外的平滑滤波器来解决粗略的初始内核问题。因此，我们提出了一种在频域中使用迭代重新加权最小二乘法（IRLS）的快速优化解决方案。我们通过几次盲解卷积来评估这些方法。图像去模糊结果的定量指标和视觉性能表明，我们的方法在图像质量恢复和计算时间成本方面比以前的方法表现更好。该方法将增加将去模糊应用于成像设备的可行性。