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Content Provider | IEEE Xplore Digital Library |
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Author | Guangling Sun Chuan Qin |
Copyright Year | 2011 |
Abstract | Image super-resolution (SR) reconstruction is one of the most popular research topics in image processing for decades. This paper presents a novel approach to deal with single image SR problem. We search a mapping between a pair of low-resolution and high-resolution image patch in gradient domain by learning a generic image database and the input image itself. Given low-resolution image, the high-resolution image is reconstructed using sparse representation in gradient domain and solving Poisson equation. Experiments demonstrate that the state-of-the-art results have been achieved compared to other SR methods in terms of both PSNR and visual perception. |
Starting Page | 24 |
Ending Page | 28 |
File Size | 574096 |
Page Count | 5 |
File Format | |
ISBN | 9781457717956 |
DOI | 10.1109/MINES.2011.126 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-11-04 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Strontium Training Interpolation Dictionaries Super-resolution Poisson equation Sparse representation Spatial resolution Image reconstruction Gradient domain Dictionary learning |
Content Type | Text |
Resource Type | Article |
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