Many imaging systems utilize detector arrays that are not sufficiently dense to sample the scene according to the Nyquist criterion. As a result of this undersampling, some of the higher spatial frequencies admitted by the optics are aliased to lower frequency representations in the sampled image. This creates undesirable artifacts that decrease the utility of the imagery. Furthermore, the blurring effects of the optics and the finite detector size also degrade the image quality. Several approaches for obtaining a higher sampling rate have been suggested in the literature such as controlled and uncontrolled microscanning or from natural camera platform motion in order to up-sample the scene. Here we extend this work to include the possibility of non-global rigid body motion. In particular, we show that the motion of rigid objects within the scene is in many cases sufficient to allow the up-sampling of the object. We present a method that makes use of optical-flow-based motion segmentation techniques to isolate moving objects from the background in a sequence of digital images. These segmented objects from each frame provide a unique 'look' or set of samples of the object, allowing us to perform a reconstruction of the object. This allows for high resolution image reconstruction of each segmented object independently. The experimental results presented illustrate the breakdown of global reconstruction algorithms in the presence of non-global rigid motion. We also present results using the proposed method that treats individual moving objects and background separately. The results include data from a visible CCD camera.