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|Title:||Image super-resolution via low-pass filter based multi-scale image decomposition||Authors:||Zhu, S.
|Issue Date:||2012||Citation:||Zhu, 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. https://doi.org/10.1109/ICME.2012.29||Abstract:||This 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.||Source Title:||Proceedings - IEEE International Conference on Multimedia and Expo||URI:||http://scholarbank.nus.edu.sg/handle/10635/70526||ISSN:||19457871||DOI:||10.1109/ICME.2012.29|
|Appears in Collections:||Staff Publications|
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