Multiframe super-resolution (SR) reconstruction of small moving objects against a cluttered background is difficult for two reasons: a small object consists completely of “mixed” boundary pixels and the background contribution changes from frame-to-frame. We present a solution to this problem that greatly improves recognition of small moving objects under the assumption of a simple linear motion model in the real-world. The presented method not only explicitly models the image acquisition system, but also the space-time variant fore- and background contributions to the “mixed” pixels. The latter is due to a changing local background as a result of the apparent motion. The method simultaneously estimates a subpixel precise polygon boundary as well as a high-resolution (HR) intensity description of a small moving object subject to a modified total variation constraint. Experiments on simulated and real-world data show excellent performance of the proposed multiframe SR reconstruction method.

Moving objects are often the most interesting parts in image sequences. When images from a camera are undersampled and the moving object is depicted small on the image plane, processing afterwards may help to improve the visibility as well as automatic recognition of the object. This paper presents an approach which performs Super-Resolution (SR) specifically on small moving objects. The presented approach estimates simultaneously a sub-pixel accurate boundary and an intensity description of the small moving object. This approach has the advantage that it permits explicit modeling of the partial volume effect for pixels which are influenced by both the moving object and the background of the scene. This paper describes the setup of the proposed SR algorithm for small moving objects and shows its superior performance in comparison with a standard SR approach.

Unlike most Super-Resolution (SR) methods described in literature, which perform only SR reconstruction on the background of an image scene, we propose a framework that performs SR reconstruction simultaneously on the background and on moving objects. After registration of the background, moving objects are detected and to each moving object registration is applied. The fusion and deblurring tasks of the framework are performed by the well-known method of Hardie. To evaluate the performance of the framework the Triangle Orientation Discrimination (TOD) method is used. The TOD method quantitatively measures the SR performance on the image background and on moving objects. From experiments it can be concluded that under proper conditions, the SR performance on moving objects is similar as the SR performance on the background.