Multi-regularization Hybrid Constraints Method for Blind Image Restoration

Image restoration is a long-standing and challenging inverse issue. In order to recover an image from its blurry version blindly, a multi-regularization hybrid constraints method is proposed. First, the large scale edges are extracted from the image with a Local Structure Extraction Scheme (LSES). Then, in the Blur Kernel (BK) estimation step, the extracted large scale edges are used for BK estimation, and a sparsity and smoothness dual-regularization constraints model proposed in the previous study, is also employed for estimating BK more accurately. In the image restoration step, a multi-regularization constraints model, which combines the Total Variation (TV) model and Shock filtering invariance, is proposed for obtaining high-quality restoration image. Finally, in order to exactly estimate the BK and simultaneously obtain high-quality restoration image, the proposed models are addressed with a half-quadratic variables splitting scheme. A large number of experiments are performed on both synthetic blurred images and real-life blurred images. The experimental results demonstrate the effectiveness of the proposed method, while in comparison with several recent representative image blind restoration methods, not only the subjective vision, but also the objective numerical measurement has obvious improvement.