An efficient projected subgradient algorithm for blind image deconvolution using an L1-TV cost function

Traditional blind image iterative algorithms are designed for Gaussian noise by using the L₂-norm error term. For robustness against the influence of non-Gaussian noise, an efficient projected subgradient algorithm for blind image deconvolution is developed, based on a TV cost function with the L₁-norm error term. Because of using the subgradient technique, the proposed subgradient algorithm can minimize the L₁-TV cost function directly. By contrast, existing L₁ norm-based image restoration algorithms only minimize the approximate L₁ cost function and assume a known blur. Illustrative examples show that under suboptimal regularization parameters, the projected subgradient algorithm is efficient in producing better image estimate than two traditional blind image iterative algorithms in terms of both ISNR and perception.