Inverse Error Function Trajectories for Image Reconstruction*This material is based upon work supported by the National Science Foundation under Grant No. 1662029

Abstract

Capturing clear images while a camera is moving fast, is integral to the development of mobile robots that can respond quickly and effectively to visual stimuli. This paper proposes to generate camera trajectories, with position and time constraints, that result in higher reconstructed image quality. The degradation in of an image captured during motion is known as motion blur. Three main methods exist for mitigating the effects of motion blur: (i) controlling optical parameters, (ii) controlling camera motion, and (iii) image reconstruction. Given control of a camera's motion, trajectories can be generated that result in an expected blur kernel or point-spread function. This work compares the motion blur effects and reconstructed image quality of three trajectories: (i) linear, (ii) polynomial, and (iii) inverse error. Where inverse error trajectories result in Gaussian blur kernels. Residence time analysis provides a basis for characterizing the motion blur effects of the trajectories.