Noisy low resolution (LR) images are always obtained in real applications, but many existing image magnification\nalgorithms can not get good result from a noisy LR image. We propose a two-step image magnification algorithm to\nsolve this problem. The proposed algorithm takes the advantages of both regularization-based method and\nlearning-based method. The first step is based on total variation (TV) regularization and the second step is based on\nsparse representation. In the first step, we add a constraint on the TV regularization model to magnify the LR image\nand at the same time to suppress the noise in it. In the second step, we propose an order-changed dictionary training\nalgorithm to train the dictionaries which is dominated by texture details. Experimental results demonstrate that the\nproposed algorithm performs better than many other algorithms when the noise is not serious. The proposed\nalgorithm can also provide better visual quality on natural LR images.
Loading....