Motion blur removal in nonlinear sensors

Abstract

We address the problem of motion blur removal from an image sequence that was acquired by a sensor with nonlinear response. Motion blur removal in purely linear settings has been studied extensively in the past. In practice however, sensors exhibit nonlinearities, which also need to be compensated for. In this paper we study the problem of joint motion blur removal and nonlinearity compensation. Two naive approaches for treating this problem are to apply the inverse of the nonlinearity prior to a deblurring stage or following it. These strategies require a preliminary motion estimation stage, which may be inaccurate for complex motion fields. Moreover, even if the motion parameters are known, we provide theoretical arguments and also show through simulations that theses methods yield unsatisfactory results. In this work, we propose an efficient iterative algorithm for joint nonlinearity compensation and motion blur removal. Our approach relies on a recently developed theory for nonlinear and nonideal sampling setups. Our method does not require knowledge of the motion responsible for the blur. We show through experiments the effectiveness of our method compared with alternative approaches.