Multi-frame image super-resolution reconstruction using sparse co-occurrence prior and sub-pixel registration
In this paper, a hybrid super-resolution (SR) method is proposed by combining the concepts of both multi-frame and single-frame SR to generate a high-resolution (HR) image. The main contributions are in two aspects: the first one is hierarchical iterative sub-pixel registration, which provides accurate registration information of input low-resolution (LR) images or frames, to generate an initial HR image; the second one is to enhance the initial HR image with sparse co-occurrence prior, resulted from specially-designed dictionaries containing patches from both generic training images and interpolated input LR images. As a whole, the proposed hybrid SR method makes use of information from both sub-pixel registration and sparse co-occurrence prior to get reconstructed SR image with large zoom-in factor. The simulation results from synthetic images and real video frames illustrate its effectiveness and the superiority in image quality over conventional multi-frame and single-frame SR methods.
super-resolution reconstruction sparse co-occurrence prior sub-pixel registration sparse representation