An edge-driven total variation approach to image deblurring and denoising

Traditional nonlinear filtering techniques are observed in underutilization of blur identification techniques, and vice versa. To improve blind image restoration, a designed edge-driven nonlinear diffusion operator and a point spread function (PSF) learning term are integrated to a total variation regularization. The cost functions are minimized iteratively in an alternate minimization with respect to the estimation of images and PSFs under these conditions. Numerical experiments show that the proposed algorithm is efficient and robust in that it can handle images that are formed in different environments with different types and amounts of blur and noise.