Super-resolution reconstruction using graph Laplacian penalization

This paper proposes to employ graph Laplacian penalization in multi-image super-resolution reconstruction. Most state-of-the-art methods use an optimization model with two items: the data fidelity item and the penalization item. However, these methods often ignore the impact of the penalization item and utilize simple formulations such as high-pass filters to fulfill the super-resolution task. As a result, they can not restore much local structural information of the high resolution image. By using graph Laplacian, the proposed method in this paper can retain more local manifold structures in the high resolution images. Based on this idea, the optimization model is constructed and the solution is presented. Comparative experiments have validated our method. The experiments have also tested our method has much faster convergence speed.