Blurry image can be represented as the convolution of a latent image and a blur kernel

so it is an ill-posed problem to solve the kernel and the latent image inversely from a single blurry image.The most effective way to solving ill-posed problem is using cost function with priori term.For blind image deblurring problem

we propose a ratio of convex norm to concave norm as a regularization priori term

which has more sparse representation ability.When solving the model by variable splitting method

we propose L1 norm fidelity term to update high-frequency information of the latent image.At the stage of updating the blurring kernel

we propose a linear increasing weight parameter to estimate the blurring kernel gradually by multi-scale approach from coarse to fine.After obtaining the blur kernel

we use a closed threshold formula to estimate the latent image.This method can obtain high-quality image efficiently.The experimental results demonstrate the effectiveness of the model and the rapidity of the algorithm.