We investigated an approach to reconstructing high-resolution images from dynamic image sequences using local spectral analysis. High-resolution reconstruction from linearly shifted multiple static image frames has previously been studied, in which the aliasing relationship between the spectrum of the original signal and the DFTs of shifted and sampled signals is exploited. In the high-resolution reconstruction of dynamic image sequences, difficulties occur a s a result of nonuniform shifts in the frames. We use local spectral analysis to achieve high-resolution reconstruction by overlapped block decomposition and motion compensation. For each block image in a reference frame in the sequence, motion estimation and subpixel registrations are performed against adjacent frames. High resolution reconstruction is performed on such motion-compensated block-image sequences. For some blocks containing motion boundaries, high resolution reconstruction is difficult; a new scene emerges or disappears producing inconsistent information. An interpolation technique is used in such blocks to generate the number of pixels consistent with other high-resolution blocks. The flower-garden image sequence is used for the computer simulations. The quality of t h e restored images are very encouraging; the aliasing effects in the original frames are significantly reduced and sharper edges are produced. The overall procedure to generate such higher-resolution images from a dynamic image sequence is described in detail. The result can be applied to various image sequence restoration applications including up-conversion of conventional video signals. 0 1995
In this paper, we propose a method of efficient computation of wavelet coefficients from DCT-based coded image/video signals. Block Transform Domain Filtering(BTDF) is well suited for transcoding of such data. First direct transform domain processing removes the necessary of inverse transform. Second, the number of nonzero elements in the blocks are significantly smaller than spatial domain. Therefore, the amount of computation can be reduced accordingly. Finally, the block processing algorithm provides a parallel processing method. Hence a fast implementation of the algorithm is well suited.
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