Single image super-resolution via 2D sparse representation
Image super-resolution with sparsity prior provides promising performance. However, traditional sparse-based super resolution methods transform a two dimensional (2D) image into a one dimensional (1D) vector, which ignores the intrinsic 2D structure as well as spatial correlation inherent in images. In this paper, we propose the first image super-resolution method which reconstructs a high resolution image from its low resolution counterpart via a two dimensional sparse model. Correspondingly, we present a new dictionary learning algorithm to fully make use of the corresponding relationship of two pairs of 2D dictionaries of low and high resolution images, respectively. Experimental results demonstrate that our proposed image super-resolution with 2D sparse model outperforms state-of-the-art 1D sparse model based super resolution methods in terms of both reconstruction ability and memory usage.