Image Deblurring Using Smartphone Inertial Sensors

Zhe Hu, Lu Yuan, Stephen Lin, Ming-Hsuan Yang; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1855-1864

Removing image blur caused by camera shake is an ill-posed problem, as both the latent image and the point spread function (PSF) are unknown. A recent approach to address this problem is to record camera motion through inertial sensors, i.e., gyroscopes and accelerometers, and then reconstruct spatially-variant PSFs from these readings. While this approach has been effective for high-quality inertial sensors, it has been infeasible for the inertial sensors in smartphones, which are of relatively low quality and present a number of challenging issues, including varying sensor parameters, high sensor noise, and calibration error. In this paper, we identify the issues that plague smartphone inertial sensors and propose a solution that successfully utilizes the sensor readings for image deblurring. With both the sensor data and the image itself, the proposed method is able to accurately estimate the sensor parameters online and also the spatially-variant PSFs for enhanced deblurring performance. The effectiveness of this technique is demonstrated in experiments on a popular mobile phone. With this approach, the quality of image deblurring can be appreciably raised on the most common of imaging devices.