Sparse representation using multiple dictionaries for single image super-resolution

New algorithms are proposed in this paper for single image super-resolution using multiple dictionaries based on sparse representation. In the proposed algorithms, a classifier is constructed which is based on the edge properties of image patches via the two lowest discrete cosine transformation (DCT) coefficients. The classifier partitions all training patches into three classes. Training patches from each of the three classes can then be used for the training of the corresponding dictionary via the K-SVD (singular value decomposition) algorithm. Experimental results show that the high resolution image quality using the proposed algorithms is better than that using the traditional bi-cubic interpolation and Yang’s method.