A sparse representation-based image resolution improvement method by processing multiple dictionary pairs with latent Dirichlet allocation model for street view images

Abstract

Street view applications are widely used in many situations. However, the resolution of the street view image is not high enough. Users always desire high resolution street view images. Image resolution improvement methods can effectively generate a high resolution street view image from a single low resolution street view image. The sparse representation-based image resolution improvement method is a promising way to improve the resolution of an image. However, only one dictionary pair, which fails to represent the diverse structures in images, is used in conventional sparse representation-based methods This may lead to poor performances in many circumstances. In this paper, we propose a new sparse representation-based method with multiple dictionary pairs. To capture the various structures at the semantic level, our method adopts latent Dirichlet allocation model to divide the patches into clusters. Then we learn a dictionary pair for each cluster. Finally, these dictionary pairs are used to reconstruct high resolution images. Experimental results validate that our method is superior over the compared methods in both visual perception and objective quantitation.