Fractional order total variation regularization for image super-resolution

In this paper, we present a fractional order total variation (TV) regularization functional for image super-resolution, the role of which is to better handle the texture details of image. This regularization functional is then incorporated into a variational formulation with an image fidelity term and the usual TV regularization that can efficiently preserve the discontinuities and image structures. The resulting evolution equation is the gradient descent flow that minimizes the overall functional. The proposed model has been applied to eight real images with promising results; unlike the existing TV-based image super-resolution models, the proposed model does not suffer from block artifacts, staircase edges and false edge near the edges.

► We proposed image super-resolution model using fractional differentiation.
► Fractional order regularization can better handle the texture information of image.
► Our model incorporates the usual TV to efficiently preserve image structures.