Single-Image Super-Resolution Using Rational Fractal Interpolation and Adaptive Wiener Filtering

In this paper, we have propounded a neoteric procedure for the super-resolution of an image using a single image. An image of low resolution is given as input which is upscaled to an image while preserving the information that is stored in textural and structural details of an image. The image provided as input which is of low resolution is segregated into two sections, namely textured and non-textured according to the features of the image. Rational fractal interpolation is employed in the section of the image considered as textured and rational interpolation is employed in the remaining image which is considered to be non-textured. Thereafter, pixel mapping is performed. The result obtained from interpolation is found to contain Gaussian noise. To subdue the effect of this noise, an adaptive Wiener filter is applied. Finally, an image of high resolution is obtained. Profound simulations and assessments demonstrate that competitive performance is achieved by our algorithm. The mean square error reduces approximately up to $$5\%$$ , whereas the structural similarity index improves marginally.