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LMMSE restoration of blurred and noisy image sequences

In this paper we propose a computationally efficient multiframe Wiener filtering algorithm, called the cross-correlated multiframe (CCMF) Wiener filtering, for restoring image sequences that are degraded by both blur and noise. The CCMF approach accounts for both intraframe (spatial) and interframe (temporal) correlations by directly utilizing power and cross-power spectra of the frames. We propose an efficient implementation of the CCMF filter which requires the inversion of only N X N matrices, where N is the number of frames used in the restoration. Furthermore, is it shown that if the auto and cross-power spectra are estimated based on a three-dimensional (3-D) multiframe autoregressive (AR) model, no matrix inversion is required. We present restoration results using the proposed approach, and compare them with those obtained by restoring each frame independently using a single-frame Wiener filter. In addition, we provide the results of an extensive study on the performance and robustness of the proposed algorithm in the case of varying blur, noise, and power and cross- power spectra estimation methods using different image sequences.