An improved joint dictionary training method for single image super resolution

Purpose - The purpose of this paper is to introduce an improved method for joint training of low- and high-resolution dictionaries in the single image super resolution. With simulations, the proposed method is thereafter evaluated. Design/methodology/approach - Sparse representations of low-resolution image patches are used to reconstruct the high-resolution image patches with high resolution dictionary. By using different factors, the scheme weights the two dictionaries in the high- and low-resolution spaces in the training process. It is reasonable to achieve better reconstructed images with more emphasis on the high-resolution spaces. Findings - An improved joint training algorithm based on K-SVD is developed with flexible weight factors on dictionaries in the high- and low-resolution spaces. From the experiment results, the proposed scheme outperforms the classic bicubic interpolation and neighbor-embedding learning based method. Originality/value - By using flexible weight factors in joint training of the dictionaries for super resolution, better reconstruction results can be achieved.