Multiframe visual-inertial blur estimation and removal for unmodified smartphones

Pictures and videos taken with smartphone cameras often suffer from motion blur due to handshake during the exposure time. Recovering a sharp frame from a blurry one is an ill-posed problem but in smartphone applications additional cues can aid the solution. We propose a blur removal algorithm that exploits information from subsequent camera frames and the built-in inertial sensors of an unmodified smartphone. We extend the fast non-blind uniform blur removal algorithm of Krishnan and Fergus to non-uniform blur and to multiple input frames. We estimate piecewise uniform blur kernels from the gyroscope measurements of the smartphone and we adaptively steer our multiframe deconvolution framework towards the sharpest input patches. We show in qualitative experiments that our algorithm can remove synthetic and real blur from individual frames of a degraded image sequence within a few seconds

Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation

This paper presents an image deblurring algorithm to remove motion blur using analysis of motion trajectories and local statistics based on inertial sensors. The proposed method estimates a point-spread-function (PSF) of motion blur by accumulating reweighted projections of the trajectory. A motion blurred image is then adaptively restored using the estimated PSF and spatially varying activity map to reduce both restoration artifacts and noise amplification. Experimental results demonstrate that the proposed method outperforms existing PSF estimation-based motion deconvolution methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed in various imaging devices because of its efficient implementation without an iterative computational structure

Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data

Image deblurring is a key component in vision based indoor/outdoor navigation systems; as blurring is one of the main causes of poor image quality. When images with poor quality are used for analysis, navigation errors are likely to be generated. For navigation systems, camera movement mainly causes blurring, as the camera is continuously moving by the body movement. This paper proposes a deblurring methodology that takes advantage of the fact that most smartphones are equipped with 3-axis accelerometers and gyroscopes. It uses data of the accelerometer and gyroscope to derive a motion vector calculated from the motion of the smartphone during the image-capturing period. A heuristic method, namely particle swarm optimization, is developed to determine the optimal motion vector, in order to deblur the captured image by reversing the effect of motion. Experimental results indicated that deblurring can be successfully performed using the optimal motion vector and that the deblurred images can be used as a readily approach to object and path identification in vision based navigation systems, especially for blind and vision impaired indoor/outdoor navigation. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. This experiment aims to identify issues in image quality including low light conditions, low quality images due to movement of the capture device and static and moving obstacles in front of the user in both indoor and outdoor environments. From this information, image-processing techniques to will be identified to assist in object and path edge detection necessary to create a guidance system for those with low vision