Single image fast deblurring algorithm based on hyper-Laplacian model

An improved single image fast deblurring algorithm based on hyper-Laplacian constraint is proposed. The algorithm is improved in three aspects: image blur kernel estimation sub-region selection, blur kernel precise estimation, and fast non-blind deconvolution. First, image amplitude and gradient are used as the basis of blur kernel estimation. On the basis of analysing the edge amplitude and gradient of the image, the image sub-region for blur kernel estimation is selected. Then the sparsity of the blur kernel is restricted by hyper-Laplacian, and the fast solving mode of alternately solving different variables is designed. The blur kernel information is accurately estimated. In the fast non-blind deconvolution restoration phase of the image, the regularised constraint term of the hyper-Laplacian model is improved and the image gradient distribution is constrained. The blind growth trend of the regional gradient near the strong edge can be suppressed well, and the deblurred image with clear edge structure is generated. Experimental results show that the proposed algorithm can achieve better image deblurring effect and high efficiency.