Output details
11 - Computer Science and Informatics
Lancaster University
Article title
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
Type
D - Journal article
Title of journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Article number
-
Volume number
32
Issue number
6
First page of article
1127
ISSN of journal
0162-8828
Year of publication
2010
URL
-
Number of additional authors
1
Additional information
<26> Image enhancement is fundamental to many industries, but requires domain-specific expertise. Our work, which is now cited as a standard reference for learning-based image enhancement, removes this requirement by allowing users to build custom image enhancement software from examples without having to acquire domain specific expertise. We solve this problem with a novel, fast way to learn models from millions of image pairs. This has high impact on single-image super-resolution and image denoising applications, and applies generally to any application-specific image enhancement algorithm. Published in IEEE PAMI (IF=5.07)
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
58
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
