The effect of dictionary learning algorithms on super-resolution hyperspectral reconstruction

The spatial resolutions of hyperspectral images are generally lower due to imaging hardware limitations. Super-resolution algorithms can be applied to obtain higher resolutions. Many algorithms exist to achieve super-resolution hyperspectral images from low resolution images acquired in different wavelengths. One of the popular algorithms is sparse representation-based algorithms that employ dictionary learning methods. In this study, a comparative framework is developed to investigate which dictionary learning algorithm leads to better super-resolution images. In order to achieve that, K-SVD and ODL dictionary learning algorithms are employed for comparison. A sparse representation-based algorithm G-SOMP+ is used for hyperspectral super-resolution reconstruction. The experimental results show that ODL algorithm outperforms K-SVD in terms of both reconstruction quality and processing times.