Image stabilization is very important in vision based indoor/outdoor navigation systems. Blurring is one main cause of poor image quality, which can be caused by a movement of the camera at the time of taking the image, a movement of objects in front, atmospheric turbulence or out-of-focus. Out of these factors, camera movement is dominant in navigation systems as the camera is continuously moving. This paper presents the preliminary results of deblurring performed using point spread function (PSF) computed using synchronized inertial sensor data. 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. This motion vector is applied to the captured image so that the effect of motion is reversed during the debrurring process. This work is a part of an indoor navigation project that aims to assist people with vision impairment. Image processing form a significant part of the proposed system and as such clearly defined edges are essential for path and obstruction identification. Different deblurring methods are compared for their performance in reversing the effect of camera movement. Results indicated that deblurring can be successfully performed using the motion vector and that the resulting 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. The paper also proposes a novel deblurring algorithm that uses PSF computed for different portions of the image to deblur that portion of the image.