An image sharpening operator combined with framelet for image deblurring

Image sharpening can highlight fine details in images but with a tendency of amplifying noise. This paper proposes a novel idea of incorporating an image sharpening operator into a framelet-based model for image deblurring. The proposed model is convex and hence it can be solved efficiently by the semi-proximal alternating direction method of multipliers (sPADMM) with guaranteed linear rate convergence, which covers the classical ADMM. The experimental results on different blurring kernels and Gaussian noise levels show that the proposed approach outperforms the state-of-the-art methods in terms of PSNR, SSIM, relative error, and visual quality.