Regularization algorithm for blind image restoration based on wavelet transform

A wavelet based adaptive regularization scheme for blind image restoration is presented. The degraded image is decomposed to obtain its wavelet coefficients in wavelet domain, and the image different frequency sub-bands are obtained also. Then, different adaptive regularization image restoration schemes are used in different sub-bands, removing blur in the low frequency sub-bands, while reducing noise and preserving edges in the high frequency sub-bands, and the algorithm finally obtains a restored image by adverse transforming. The experiments show that the mean square error (MSE) has a reduction of 1.60, while the signal to noise ratio (SNR) is increased by 1.76. It demonstrates that the blind image restoration method is more efficient compared with traditional space-adaptive regularization method.