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We present a novel space-time patch-based method for image sequence restoration. We propose an adaptive statistical estimation framework based on the local analysis of the bias-variance trade-off. At each pixel, the space-time neighborhood is adapted to improve the performance of the proposed patch-based estimator. The proposed method is unsupervised and requires no motion estimation. Nevertheless, it can also be combined with motion estimation to cope with very large displacements due to camera motion. Experiments show that this method is able to drastically improve the quality of highly corrupted image sequences. Quantitative evaluations on standard artificially noise-corrupted image sequences demonstrate that our method outperforms other recent competitive methods. We also report convincing results on real noisy image sequences.
Image sequence restoration, denoising, nonparametric estimation, nonlinear filtering, bias-variance trade-off.

P. Bouthemy, C. Kervrann and J. Boulanger, "Space-Time Adaptation for Patch-Based Image Sequence Restoration," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 1096-1102, 2007.
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