Image deconvolution using multigrid natural image prior and its applications

The natural image prior has been proven to be a powerful tool for image deblurring in recent years, though its performance against noise in various applications has not been thoroughly studied. In this paper, we present a multigrid natural image prior for image deconvolution that enhances its robustness against noise, and afford three applications of image deconvolution using this prior: deblurring, super-resolution, and denoising. The prior is based on a remarkable property of natural images that derivatives with different resolutions are subject to the same heavy-tailed distribution with a spatial factor. It can serve in both blind and non-blind deconvolutions. The performances of the proposed prior in different applications are demonstrated by corresponding experimental results.