Single image super resolution based on multiscale local similarity and neighbor embedding
详细信息 查看全文
- 作者：Lulu PanAuthor Vitae ; Guohua PengAuthor Vitae ; Weidong YanAuthor Vitae ; Hongchan ZhengAuthor Vitae
- 关键词：Super resolution ; Multiscale local self similarity ; Neighbor embedding ; Iterate errors
- 刊名：Neurocomputing
- 出版年：2016
- 出版时间：26 September 2016
- 年：2016
- 卷：207
- 期：Complete
- 页码：250-263
- 全文大小：7775 K
文摘
Image quality and algorithm efficiency are the two core problems of super resolution (SR) from a single image. In this paper, we propose a novel single image SR method by using multiscale local similarity and neighbor embedding method. The proposed algorithm utilizes the self similarity redundancy in the original input image, and does not depend on external example images or the whole input image to search and match patches. Instead, we search and match patches in a localized region of the image in each level, which can improve the algorithm efficiency. The neighbor embedding method is used to generate more accurate patches for reconstruction. Finally, we use the original image and filters we design to control the iterate errors which caused by layered reconstruction, which can further improve the quality of SR results. Experimental results demonstrate that our method can ensure the quality of SR images and improve the algorithm efficiency.