A blind image deconvolution method based on noise variance estimation and blur type reorganization

Restoring the observed image suffering from blur and noise simultaneously is a challenging problem. It may cause heavy estimating error of blur and noise parameters. In this paper, a novel blind image deconvolution approach based on noise variance estimation and blur type reorganization is presented. This method first performs the noise variance estimation from the noisy blurred image. Then, using the property that the certain blur may lead to the specific frequency component distortion of the image Fourier spectrum, the blur type can be reorganized. After this, according to the reorganized blur type, the blur coefficients can be computed more efficiently by minimizing the objective function based on autoregressive moving average (ARMA) model. And the restored image is obtained with least-square filter. We demonstrate the proposed method in experiments with blurred texture images.