An improved joint dictionary training method for single image super resolution

Abstract

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.

Citation

Zeng, L., Li, X. and Xu, J. (2013), "An improved joint dictionary training method for single image super resolution", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 32 No. 2, pp. 721-727. https://doi.org/10.1108/03321641311297142