Nonblind image deblurring by total generalized variation and shearlet regularizations

Image deblurring is one of the classical problems in image processing and computer vision. The vital task is to restore the high-quality image with edge-preservation, details-protection, and artifacts suppression. In order to achieve ideal results, a nonblind image deblurring method that combines the total generalized variation (TGV) and the shearlet-based sparsity is proposed. First, the observed image is decomposed into two components: structures and details by a global gradient extraction scheme. Second, for the structure component, the TGV regularization is utilized to eliminate the staircase effects and avoid edge blurring. Meanwhile, the shearlet-based sparsity is applied on the detail component to preserve the texture details. At last, in the alternating direction framework, the split Bregman and the primal-dual algorithms are alternatively employed to optimize the proposed hybrid regularization model. Numerical experiments demonstrate the efficiency and viability of the proposed method for eliminating the aliasing artifacts while preserving the salient edges and texture details. (C) 2017 SPIE and IS&T