Advance Neighbor Embedding for Image Super Resolution

This paper presents the Advance Neighbor embedding (ANE) method for image super resolution. The assumption of the neighbor-embedding (NE) algorithm for single-image super-resolution Reconstruction is that the feature spaces are locally isometric of low-resolution and high-resolution Patches. But, this is not true for Super Resolution because of one to many mappings between Low Resolution and High Resolution patches. Advance NE method minimize the problem occurred in NE using combine learning technique used to train two projection matrices simultaneously and to map the original Low Resolution and High Resolution feature spaces onto a unified feature subspace. The Reconstruction weights of k- Nearest neighbour of Low Resolution image patches is found by performing operation on those Low Resolution patches in unified feature space. Combine learning use a coupled constraint by linking the LR?HR counterparts together with the k-nearest grouping patch pairs to handle a large number of samples. So, Advance neighbour embedding method gives better resolution than NE method