Image denoising via sparse representations over Sequential Generalization of K-means (SGK)

Abstract

We have recently proposed a Sequential Generalization of K-means (SGK) to train dictionary for sparse representation. SGK's training performance is as effective as the standard dictionary training algorithm K-SVD, alongside it has a simpler implementation to its advantage. In this piece of work, through the problem of image denoising, we are making a fair comparison between the usability of SGK and K-SVD. The obtained results suggest that we can successfully replace K-SVD with SGK, due to its quicker execution and comparable outcomes. Similarly, it is possible to extend the use of SGK to other applications of sparse representation.