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Image interpolation based on non-local geometric similarities and directional gradients

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)1707-1719
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number9
Publication statusPublished - 1 Sep 2016
Publication typeA1 Journal article-refereed


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.


  • Directional gradient, geometric similarity, image interpolation, minimum mean square error (MMSE)

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Field of science, Statistics Finland