Motion deblurring using normalized nonlinear diffusion regularization

Motion deblurring is a hard and hot topic which has been made significant progress in the last decade. It is hard because it is an ill-posed problem for both kernel and latent image estimation. In this paper, we use normalized nonlinear diffusion regularization for motion deblurring. By using nonlinear diffusion method, our method can deal with edges adaptively. Normalized nonlinear diffusion term can make the energy decreasing while in the solving process and the minimum of the cost function is the real solution. Because the result derived from the energy function give the lowest cost, our method does not need any auxiliary method for solving the energy function besides multiscale implementation. Using the estimated kernel, the final clear image can be got by total variation method. Experiments demonstrate the validity of the proposed method.