Single image super resolution via texture constrained sparse representation

Image super resolution is a challenging highly ill-posed inverse problem. In this paper, we proposed a texture constrained sparse representation for single image super resolution. Firstly, the low resolution observed image is segmented into different texture regions. Through preprepared texture databases, the low resolution regions are classified into different texture categories using the designed texture classifier. Then, the high resolution segments are reconstructed by sparse representation with relevant texture dictionaries. Integrating all segments, the high resolution result is obtained. The proposed method is compared with sparse representation method and some existing methods. The experimental results show that our method achieves better results in visual inspection and quantitative analysis.