Please use this identifier to cite or link to this item:
|Title:||Image super-resolution via low-pass filter based multi-scale image decomposition|
|Source:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 13, 2017
checked on Dec 16, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.