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Image restoration plays a major role in improving image quality as a preprocessing step in various imaging systems. While conventional image restoration techniques, such as the constrained least-squares (CLS) and the Wiener filters, often exhibit either noise amplification or over-smoothing problem. On the other hand, advanced image restoration techniques, such as the iterative regularization and the combined Fourier and wavelet domain thresholding filters, are not suitable for real-time applications because of high computational overhead. For overcoming these problems, we present a novel vaguelette-wavelet decomposition (VWD) approach to real-time, space-frequency-adaptive image restoration based on frequency-adaptive shrinkage and directional wavelet bases. The proposed algorithm effectively reduces noise by adaptively shrinking wavelet coefficients based on alpha map and entropy, and restores a degraded image using the vaguelette-wavelet transform. In generating vaguelettes, we use the estimated point-spread function (PSF) and new directional wavelet bases. The extended set of experimental results confirm that the proposed algorithm can restore sharp details without amplifying noise, and is suitable for commercial low-cost, high-quality imaging devices, such as digital and computational cameras, visual surveillance systems, and consumer's camcorders.