An improved regularization-based method of blur kernel estimation for blind motion deblurring

In engineering application, image blur is a troublesome problem due to camera shaking or relative motion. To address it, this paper proposes an efficient regularization method for blind motion deblurring of image. In the process of estimating blur kernel. The optimization energy function is improved. Smooth L0 norm is introduced instead of L0 norm to facilitate the optimization of energy function. Besides, a regular term is reduced so that the corresponding parameter is saved as well. In addition, an optimization algorithm is presented to automatically solve the optimal values of parameters through simulated annealing. Experimental results show that the proposed method is able to accurately estimates blur kernel and generate satisfactory retrieved images. Compared with the deep learning-based methods, the proposed method is simpler, faster and less dependent on hardware, which is easier to be implemented on embedded devices so that can be better applied to areas such as UAV photography.