Knowledge Commons of Institute of Automation,CAS
Edge-directed single image super-resolution via cross-resolution sharpening function learning | |
Han, Wei1,2; Chu, Jun1,2; Wang, Lingfeng3![]() ![]() | |
发表期刊 | MULTIMEDIA TOOLS AND APPLICATIONS
![]() |
2017-04-01 | |
卷号 | 76期号:8页码:11143-11155 |
文章类型 | Article |
摘要 | Edge-directed single image super-resolution methods have been paid more attentions due to their sharp edge preserving in the recovered high-resolution image. Their core is the high-resolution gradient estimation. In this paper, we propose a novel cross-resolution gradient sharpening function learning to obtain the high-resolution gradient. The main idea of cross-resolution learning is to learn a sharpening function from low-resolution, and use it in high-resolution. Specifically, a blurred low-resolution image is first constructed by performing bicubic down-sampling and up-sampling operations sequentially. The gradient sharpening function considered as a linear transform is learned from blurred low-resolution gradient to the input low-resolution image gradient. After that, the high-resolution gradient is estimated by applying the learned gradient sharpening function to the initial blurred gradient obtained from the bicubic up-sampled of the low-resolution image. Finally, edge-directed single image super-resolution reconstruction is performed to obtain the sharpened high-resolution image. Extensive experiments demonstrate the effectiveness of our method in comparison with the state-of-the-art approaches. |
关键词 | Super-resolution Gradient Magnitude Transformation Linear Transformation Function |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1007/s11042-016-3656-z |
关键词[WOS] | RECONSTRUCTION ; LIMITS |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61263046 ; 61403376 ; 61175025) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000400570400048 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/15262 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
作者单位 | 1.Nanchang Hangkong Univ, Inst Comp Vis, Nanchang, Jiangxi, Peoples R China 2.Nanchang Hangkong Univ, Key Laborator Jiangxi Prov Image Proc & Pattern R, Nanchang, Jiangxi, Peoples R China 3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Han, Wei,Chu, Jun,Wang, Lingfeng,et al. Edge-directed single image super-resolution via cross-resolution sharpening function learning[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2017,76(8):11143-11155. |
APA | Han, Wei,Chu, Jun,Wang, Lingfeng,&Pan, Chunhong.(2017).Edge-directed single image super-resolution via cross-resolution sharpening function learning.MULTIMEDIA TOOLS AND APPLICATIONS,76(8),11143-11155. |
MLA | Han, Wei,et al."Edge-directed single image super-resolution via cross-resolution sharpening function learning".MULTIMEDIA TOOLS AND APPLICATIONS 76.8(2017):11143-11155. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
1-5.pdf(5043KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论