A two-dimensional iterative algorithm for blind image restoration based on an L1 regularization approach

In this paper a new two-dimensional method for blind image restoration, based on an L
₁
regularization cost function is presented. A generalized gradient algorithm is proposed by using a weak derivative of the absolute value function to deal with the non-differentiable case. Unlike the double regularization (DR) approach, the proposed method uses the L
₁
estimation and is suitable for blind image restoration under non-Guassian noise environments. Compared with the NAS-RIF approach, the proposed method doesn't require the image object with a known support. Experimental results show that the proposed algorithm can obtain a better image estimate with a faster speed than three conventional blind image restoration algorithms.