Image Super-Resolution Reconstruction Based on Compressed Sensing
Shousen Chen, Jian Quanzhu, Qiang Xu · 2017
An improved super-resolution image reconstruction algorithm based on dictionary-learning is studied for the time-consuming algorithms in the existing dictionary training process. In this paper, the reconstruction of image super resolution is realized from the compressed sensing theory. The image patches are conveyed by sparse linear representations with an over-complete dictionary. In the process of training the two over-complete dictionaries of high and low resolution image, the K-SVD algorithm is applied. Experiment results show that the algorithm can not only reduce the time of the dictionary training effectively, but also improve the quality of the reconstruction of high-resolution images