Super-resolution image reconstruction using a two-dimensional subgradient algorithm

In order to reduce greatly computational cost and storage requirement, three two-dimensional (2D) gradient algorithms for fast super-resolution image reconstruction was presented by minimizing the smooth cost function. To enhance estimation accuracy of super resolution image reconstruction, this paper proposes a 2D subgradient algorithm for super-resolution image reconstruction. Compared with the existing 2D gradient algorithms, the proposed 2D subgradient algorithm can minimize a novel nonsmooth cost function with an l
₁
-norm learning term and TV term. Simulation results show that the proposed 2D subgradient algorithm has better performance in terms of both PSNR and visual quality than the existing 2D gradient algorithms.