Single image superresolution using E-SPLINE functions

Single image super resolution has become an important topic in many imaging applications especially that it has a more constrained environment than regular multi image super resolution. In this paper we propose a novel single image super resolution technique that can significantly enhance a single image based on local data and statistics without a need for any prior information. The proposed algorithm is based on injecting Laplacian energy in high frequency details and estimating missing information from matched other parts of the image. Interpolation and zooming are achieved for the initial low resolution image through Exponential spline (E-spline) interpolation followed by Independent Component Analysis (ICA) separation to enhance the quality of the final high resolution image. A detailed comparison between E-spline and B-spline interpolation for the output image as well as another comparison with recent literature techniques are presented. Further enhanced results with ICA processing are also demonstrated.