Multi-Regularization-Constrained Blur Kernel Estimation Method for Blind Motion Deblurring

Abstract

Blur kernel (BK) estimation is the crucial technique to guarantee the success of blind image deblurring. In this paper, we propose a multi-regularization-constrained method to estimate an accurate BK from a single motion-blurred image. First, in order to generate sharp and reliable intermediate latent results, we propose a model which combines the spatial scale, ${L} _{0}$ norm, and the dark channel prior. Second, in order to preserve the continuity and the sparsity, and to remove the flaw in the BK, a dual-constrained regularization model, which combines the ${L} _{0}$ -regularized intensity prior and the ${L} _{2}$ -regularized gradient prior, is proposed for accurate BK estimation. The proposed model can not only preserve the continuity and the sparsity of the BK very well but also can remove the flaw thoroughly. Finally, we propose an efficient optimization strategy which can solve the proposed model efficiently. Extensive experiments compared with the state-of-the-art methods demonstrate that our method estimates more accurate BKs and obtains higher quality deblurring images in terms of both subjective vision and quantitative metrics.