Efficient sparse representation based image super resolution via dual dictionary learning
Super resolution is of great use in many visual media related scenarios, such as displaying low resolution contents on High-Definition TV(HD-TV). In these scenarios, the efficiency of the super resolution process is of vital importance. This paper presents a fast learning based super-resolution method. The proposed method speeds up the sparse representation based super-resolution method by learning a dual dictionary, and replaces the sparse recovery step by simple matrix multiplication, which is much more computationally efficient. Experiments demonstrate that the proposed method can generate desirable super-resolved images with significant computational advantages.