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This paper presents a neighborhood dependent components based feature learning (NDCFL) for regression analysis in single image super-resolution. Given a low resolution input, the method uses directional Fourier phase feature components to adaptively learn the regression kernel based on local covariance to estimate the high resolution image. Although this formulation resembles other regression and covariance based methods, our method uses image features to learn the local covariance from geometric similarity between low resolution image and its high resolution counterpart. For each patch in the neighborhood, we estimate four directional variances to adapt the interpolated pixels. Experimental results show that the proposed algorithm performs better than other state of the art techniques especially at higher resolution scales.