Sparsity and smoothing multi-regularization constraints for blind image deblurring

作者：Tang Shu; Gong Wei Guo^*; Zhong Jian Hua

来源：Journal of Software, 2013, 24(5): 1143-1154.

DOI：10.3724/SP.J.1001.2013.04256

• Blind image resoration
• Motion blurring
• Multi-regularization constraint
• Variable splitting
• Weighted total-variation-norm

In order to recover the linear spatial invariant blurred image blindly, a multi-regularization constraint- method for blind image resoration, which is based on the sparsity and the smoothing of the motion blur point spread MPSF), is proposed. First, according to the sparsity of the edges in the natural image, a weighted total variation norm (WTV-norm) is applied to the restoration image. Next, inspired from the characteristics of the MPSF, multi-regularization constraints that handle a wide variety of blurring degradations, it is applied to the blur kernel. Finally, a modified variable splitting (MVS) method is proposed to restore the image and simultaneously estimate exactly the blur function. A large number of experimental results indicate that the proposed method can also undo a wide variety of blurring degradations (e.g. motion blur, Gaussian blur, uniform blur, disk blur). In comparison with several recent representative image blind restoration methods, the subjective vision is not only more effective, but the ISNR also improved between 1.20 dB and 4.22 dB.

• 出版日期2013