Image Deblurring Utilizing Inertial Sensors and a Short-Long-Short Exposure Strategy

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.