Single Image Super-Resolution Method via Refined Local Learning

In this paper,we propose a refined local learning scheme to reconstruct a high resolution(HR)face image from a low resolution(LR)observation.The contribution of this work is twofold.Firstly,multi-direction gradient features are extracted to search the nearest neighbors for each image patch,then the non-negative matrix factorization(NMF)is used to reduce the complexity in weight calculation,and the initial HR embedding is estimated from the training pairs by preserving local geometry.Secondly,a global reconstruction constraint and post-processing by non-local filtering is incorporated into super-resolution(SR)reconstruction process to reduce the image artifacts and further improve the image visual quality.Experimental results show that the proposed algorithm improves the SR performance both in subjective and objective assessments compared with several existing methods.

In this paper, we propose a refined local learning scheme to reconstruct a high resolution （HR） face image from a low resolution （LR） observation. The contribution of this work is twofold. Firstly, multi-direction gradient features are extracted to search the nearest neighbors for each image patch, then the non-negative matrix faetorization （NMF） is used to reduce the complexity in weight calculation, and the initial HR embedding is estimated from the training pairs by preserving local geometry. Secondly, a global reconstruction constraint and post-processing by non-local filtering is incorporated into super-resolution （SR） reconstruction process to reduce the image artifacts and further improve the image visual quality. Experimental results show that the proposed algorithm improves the SR performance both in subjective and objective assessments compared with several ex- isting methods.