Space-Variant Text Image Deblurring with Nonconvex Constraint

Due to the relative motion between camera and scene to be photographed during exposure, image deblurring is commonly categorized into two types: space-invariant and space-variant versions. Space-variant image deblurring is more difficult since the blurring effect cannot be modeled by convolution. In this paper, we mainly focus on the problem of space-variant text image deblurring. To guarantee high-quality imaging, hyper-Laplacian prior is exploited to model the distribution of text image gradients. Under the maximum a posteriori (MAP) estimation framework, we propose to develop a nonconvex variational model to handle the problem of space-variant text image deblurring. The proposed method has the capacity of suppressing the undesirable space-variant blurring effect and ringing artifacts while preserving the main structural features. Several experiments will be conducted to compare our method with two popular image deblurring methods. Numerical results have demonstrated the superior performance of the proposed method in terms of PSNR, ISNR, MSSIM and visual quality assessments.