If a point on an object passes over two or more photoreceptors during image acquisition, a blur will occur. Under these conditions, an object or scene is said to move fast relative to the camera’s ability to capture the motion. This work considers the iterative restoration of images blurred by distinct, fast-moving objects in the frames of a (video) image sequence. Even in the simplest case of fast object motion, the degradation is spatially variant with respect to the image scene. Unlike previous work, the blur is allowed to vary at each pixel. A robust (edgepreserving) iterative approach is used that requires complete knowledge of the blur point spread function to restore the scene. The blur of a moving object in a single frame is often underspecified. An estimate is obtained of the blur point spread function (PSF) to within a constant scaling factor using motion information from the displacement vector field for that frame. The iterative restoration approach used allows for the incorporation of prior knowledge of the scene structure to facilitate the restoration of difficult scenes. A bilinear approximation to the continuous PSF derived from the motion estimate to restore noisy (MPEG degraded) motion-blurred image sequences is proposed. The results of this work reinforced the well-known flexibility of iterative restoration approaches. The proposed approach is shown to be an important processing tool for the enhancement of motion-degraded frames in a digital video sequence.