Multi-scale single image self-example-based super resolution based on adaptive kernel regression

摘要

Recently self-similarity has been used for super resolution which generates favorable results. In this paper, single image super resolution method using self-example-based method is proposed. Patch redundancy cross-scale images is fully considered and patch similarity in image pyramids is used to improve the image resolution. Also the local structural constraints with steering kernel regression for patch similarity are used in the image reconstruction. For avoiding over-smoothing the structure of image, an automatic metric is presented to preserve the structure better. The patch self-similarity and local structure regularity in the image pyramids are combined to get the high resolution image. The results show that the proposed method has higher quality as compared to other state-of-art super resolution methods.