The two recent effective image super resolution algorithms, Anchored Neighborhood Regression (ANR) and its improved variation, Adjusted ANR (A+), have provided state-of-the-art performance and fast execution speed. In this paper, we further explored the improvable points of A+ and proposed the enhanced ANR (EA) algorithm. First, for a better approximation of input image patches, EA learns the dictionary with least sparsity limit to ensure more patches adhere to these atoms. Second, EA uses the correlation rather than the Euclidean distance to measure the similarity between training sample patches and dictionary atoms when searching neighbors. Third, sample patches are not only clustered to compute the regressors, but also seen as potential atoms in our EA. These potential atoms work together with dictionary atoms, help find the atom most similar with the input image patch as far as possible. We demonstrate the results on commonly used datasets, showing both better visual performance and higher index values compared to state-of-the-art methods.
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