Abstract: It is an important topic to reconstruct a high resolution image from a low resolution image.
Yang proposed an image super-resolution reconstruction algorithm based on the joint dictionary-learning,
which needs large samples, and dictionary training methods are complicated. In this paper, a new algorithm
of image super-resolution reconstruction based on MOD dictionary-learning is proposed, a small amount
of training samples is firstly used to replace large numbers of training samples of Yangs, then the MOD
dictionary-learning algorithm is used instead of Yangs FFS dictionary-learning algorithm, at last, the
resulted dictionary is applied to the image sparse representation and super-resolution reconstruction. The
experimental results show that the image reconstruction speed is improved greatly with better
reconstruction quality.

Key words: image processing, image reconstruction, joint dictionary, super-resolution reconstruction, MOD