Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the applications that motivate superresolution (SR) image and video reconstruction from low-resolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes (i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and (ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion.

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We propose a novel adaptive spatiotemporal filter, called the adaptive weighted averaging (AWA) filter, for effective noise suppression in image sequences without introducing visually disturbing blurring artifacts. Filtering is performed by computing the weighted average of image values within a spatiotemporal support along the estimated motion trajectory at each pixel. The weights are determined by optimizing a well-defined mathematical criterion, which provides an implicit mechanism for deemphasizing the contribution of the outlier pixels within the spatiotemporal filter support to avoid blurring. The AWA filter is therefore particularly well suited for filtering sequences that contain segments with abruptly changing scene content due to, for example, rapid zooming and changes in the view of the camera. The performance of the proposed AWA filter is compared with that of the spatiotemporal, local linear minimum mean square error (LMMSE) filtering., The results demonstrate that the proposed AWA filter outperforms the LMMSE filter, especially in the cases of low signal-to-noise ratios and abruptly varying scene content.