Adaptive regularized image interpolation using a probabilistic gradient measure

An adaptive image interpolation approach is proposed in this paper. The proposed approach imposes a regularization on the reconstructed high-resolution image to suppress the noise and blurring incurred in the observed low-resolution image. Furthermore, the proposed regularization scheme is steered by the local gradient information of the image, which is evaluated using a probabilistic measure. Experiments are conducted to demonstrate the superior performance of the proposed approach.