Regularized single-image super-resolution based on progressive gradient estimation
Lejun Yu, Xiaoyu Wu, Feng‐Xiang Ge, Bo Sun, Jun Yi Derek He, Robert Sablatnig · 2015
Gradient domain optimization is widely used in regularized image super-resolution, in which the gradient of high resolution (HR) is estimated for calculating the regularization energy. In this paper, a progressive gradient estimation (PGE) is proposed. In PGE, the gradient of the reconstructed HR image in the previous round of optimization is taken as the estimated gradient in the current round. Then, the estimated image gradient is progressively improved. When the estimated image gradient converges, a high quality HR image can be reconstructed. Experimental results show that the reconstructed HR images by PGE have good qualitative and quantitative performances.