Journals & Publications

Title: Edge-Directed Single Image Super-Resolution via Cross-Resolution Sharpening Function Learning  

Authors: Han, W; Chu, J; Wang, LF; Pan, CH 

Author Full Names: Han, Wei; Chu, Jun; Wang, Lingfeng; Pan, Chunhong 

Source: MULTIMEDIA TOOLS AND APPLICATIONS, 76 (8):11143-11155; 10.1007/s11042-016-3656-z APR 2017 

Language: English 

Abstract: 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. 

ISSN: 1380-7501  

eISSN: 1573-7721  

IDS Number: ET8SE  

Unique ID: WOS:000400570400048 

*Click Here to View Full Record