Image Interpolation Based on Non-local Geometric Similarities and Directional Gradients

Image interpolation offers an efficient way to compose a high-resolution (HR) image from the observed low-resolution (LR) image. Advanced interpolation techniques design the interpolation weighting coefficients by solving a minimum mean-square-error (MMSE) problem in which the local geometric simila...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2016-09, Vol.18 (9), p.1707-1719
Main Authors: Shuyuan Zhu, Bing Zeng, Liaoyuan Zeng, Gabbouj, Moncef
Format: Article
Language:English
Subjects:
Analogies
Construction
Directional gradient
geometric similarity
Image edge detection
image interpolation
Interpolation
Least squares method
Mathematical model
Mean square error methods
minimum mean square error (MMSE)
Multimedia
Robustness
Signal processing algorithms
State of the art
Weighting
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Summary:Image interpolation offers an efficient way to compose a high-resolution (HR) image from the observed low-resolution (LR) image. Advanced interpolation techniques design the interpolation weighting coefficients by solving a minimum mean-square-error (MMSE) problem in which the local geometric similarity is often considered. However, using local geometric similarities cannot usually make the MMSE-based interpolation as reliable as expected. To solve this problem, we propose a robust interpolation scheme by using the nonlocal geometric similarities to construct the HR image. In our proposed method, the MMSE-based interpolation weighting coefficients are generated by solving a regularized least squares problem that is built upon a number of dual-reference patches drawn from the given LR image and regularized by the directional gradients of these patches. Experimental results demonstrate that our proposed method offers a remarkable quality improvement as compared to some state-of-the-art methods, both objectively and subjectively.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2016.2593039