Image Super-resolution Reconstruction Algorithms Based on Self-similarities and Dictionary Learning
Yanxin Wei · Guangdian gongcheng · 2013
Super-resolution reconstruction plays an important role in reconstructing the image details and improving the visual perception.In the most of the conventional learning-based super-resolution,prior knowledge of the input image itself or natural images database is used to solve the super-resolution problem,so the quality of reconstructed images can be further improved.To reach this goal,the information of the image itself and the natural images database are combined.Firstly,the self-similarities across different image scales can be exploited to construct an image pyramid,and the high resolution image is reconstructed only by the input.After that,we learn a dictionary from natural image patches and reconstruct the initial reconstruction one,which is regarded as the input.In the back processing,non-local similarity and iterative back-projection are exploited to further improve the quality.The experiments show that the proposed algorithm achieves better results than other learning-based algorithms in terms of both visual perception and peak signal-to-noise ratio.