Dynamics-based motion deblurring for a biologically-inspired camera positioning mechanism

This paper presents a new dynamics-based method for image processing in coordination with rapid ocular movement. A camera positioning mechanism with piezoelectric cellular actuators will be employed to demonstrate the effectiveness of this approach. There are a number of mechanisms for automatic camera positioning, but few have much in common with the human ocular positioning system. When a rapid point-to-point motion is created in a camera positioner, like human saccadic motion, the image sensor receives blurry images. Existing image techniques rely solely on obtained images, or use external sensors to estimate a blur kernel, or point spread function (PSF), for motion deblurring. Inspired by recent oculomotor studies, this paper proposes a method for estimating PSFs directly from the system dynamics of a camera positioning mechanism without motion sensors. The proposed method has been evaluated by using a single degree-of-freedom camera orientation system. Results are compared with conventional methods in terms of speed and accuracy.