LRI: A low rank approach to non-local sparse representation for image interpolation

LRI: A low rank approach to non-local sparse representation for image interpolation

Zhang, Mingli and Qu, Qiang and Nobari, Sadegh and Desrosiers, Christian

Proceedings of the International Joint Conference on Neural Networks 2016

Abstract : The sparse representation models for image super-resolution have shown great potential in various imaging and vision tasks. However, most of them are challenged by the accuracy issue especially when images are significantly down-sampled. In this paper, we aim to improve the performance of sparse representation. We propose to incorporate a low rank approach into image non-local sparse representation model. To the best of our knowledge, this is the first work to integrate low rank approaches into non-local spare representation for image interpolation. The proposed method can obtain good estimation of sparse coefficients of original images. Experimental results show the effectiveness of our proposed method compared with the state-of-the-art.