Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries

Abstract EN

Sparse representation has recently attracted enormous interests in the field of image super-resolution.

The sparsity-based methods usually train a pair of global dictionaries.

However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction.

In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution.

For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features.

For each cluster, a pair of Direction and Edge dictionaries is learned.

Sparse coding is combined with the Direction and Edge dictionaries to realize super-resolution.

The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors.

Therefore, we combine the two methods to realize multiframe super-resolution.

Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method.

Experimental results demonstrate that our method can recover better edge structure and details.