Image blind deblurring using L0 sparse regularization
Zhu Qing Cheng, Yue Zhou · Chinese Journal of Stereology and Image Analysis · 2015
Image blind deblurring aims to estimate the blur kernel from the input blurry image and to restore the latent sharp one,which is a typically ill-posed problem. In order to make it more robust and reliable,many methods are proposed to formulate the cost function by representing the sparsity priors. In this paper,we introduce a framework applying L0 and L1 norm sparsity for the latent image and blur kernel respectively. Effective numerical approaches including split Bregman and shrinkage thresholding methods are used in the optimization. The proposed scheme is proved to have better restoration quality and speed comparing with other methods used in the experiment.