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dc.titleImage super-resolution via low-pass filter based multi-scale image decomposition
dc.contributor.authorZhu, S.
dc.contributor.authorZeng, B.
dc.contributor.authorYan, S.
dc.identifier.citationZhu, S.,Zeng, B.,Yan, S. (2012). Image super-resolution via low-pass filter based multi-scale image decomposition. Proceedings - IEEE International Conference on Multimedia and Expo : 1045-1050. ScholarBank@NUS Repository. <a href="" target="_blank"></a>
dc.description.abstractThis paper presents a spatial-varying minimum mean square error (MMSE)-based approach to construct super-resolution images from single source image of a lower resolution. The unique feature of this approach is that it works on a set of sub-images (also called multi-scale images) that are generated via decomposing the original source image. To do the decomposition, we design a number of low-pass filters with overlapped pass-bands so that sub-images are correlated with each other. Then, an MMSE-based estimation, involving all sub-images, is solved (after making use of the geometric-duality principle) to construct each missing pixel in the super-resolution image. Experimental results show that our new method offers a clearly-noticeable improvement over the existing MMSE-based methods (without decomposition). We believe that this is mainly attributing to the fact that both intra-scale and inter-scale correlations among the sub-images have been utilized in our approach. © 2012 IEEE.
dc.subjectimage decomposition
dc.subjectImage super-resolution
dc.subjectlow-pass filters
dc.subjectMMSE estimation
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleProceedings - IEEE International Conference on Multimedia and Expo
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