Example-based image super-resolution via blur kernel estimation and variational reconstruction

Abstract Single image super-resolution aims at generating clear high-resolution image from one low-resolution image. Due to the limited low-resolution information, it is a challenging task to restore clear, artifacts-free image, meanwhile preserving finer structures and textures. This paper proposes an effective example-based image super-resolution method while making clear image and no compromise on quality. Firstly, the image prior is imposed on the anchor neighborhood regression model to optimize mapping coefficient for interim latent image construction. In order to remove its blur, kernel estimation iteration optimization algorithm is proposed based on the salient edges which are extracted through texture-structure discriminate minimum energy function and fractional order mask enhancement. Finally, an accurate reconstruction constraint combined with a simple gradient regularization is applied to reconstruct the super-resolution image. The proposed method is able to produce clear high-frequency texture details and maintain clean edges even under large scaling factors. Experimental results show that the proposed method performs well in visual effects and similarities. Furthermore, we test our algorithm in multi-texture images for robust evaluation. It is demonstrated that our algorithm is robust under complicated textures condition.