Multiframe Super-Resolution Reconstruction of Small Moving Objects
article
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
Experiments on simulated and real-world data show excellent performance of the proposed multiframe SR reconstruction method
TNO Identifier
410630
Source
IEEE Transactions on Image Processing, 19(November), pp. 2901-2912.
Pages
2901-2912
Files
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