Learning Multi-level Local Phase Relationship for Single Image Resolution Enhancement

In this paper, a novel approach for image spatial resolution enhancement based on multi-level local Fourier phase features is proposed. This method uses adaptive kernel regression technique based on multi-level local covariance to estimate the high resolution image from a low resolution input. However, this concept is similar to other regression and covariance based methods, our method uses multi-level Fourier image features to learn the local covariance from geometric similarity between low resolution image and its corresponding high resolution image. For each local region, four weighted integrated directional variances are estimated to adapt the interpolated pixels. This method is tested on various natural and aerial images at higher resolution scales. The results confirm that the proposed technique performs better especially at high resolution scales in comparison with other state of art techniques.