Motion blur is a common problem for machine vision applications in legged mobile robotic platforms. Due to the oscillatory walking nature of these platforms, the cameras on them experience disturbances so that most of the captured frames are motion blurred. Motion blur results in loss of information in individual image frames and therefore makes single-frame deblurring an ill-posed problem. The variation in the magnitude of motion blur in consecutive video frames can be exploited to better restore these frames by combining the information available in them. The authors focus on the multi-frame image deblurring problem and propose a promising method to achieve this information fusion especially when the video is corrupted by non-linear blur, such as the case in legged mobile robots. The authors present the theory and results obtained on a realistic motion-blur dataset collected on a representative robotic platform. These results are also compared with a leading single-frame deblurring method and show improvement in particular for video corrupted by linear and non-linear motion blur.