Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11617
Title: Example selective and order independent learning-based image super-resolution
Authors: Chen, M
Qiu, GP
Lam, KM 
Issue Date: 2005
Source: Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, 2005 : ISPACS 2005, 13-16 December 2005, p. 77-80
Abstract: In this paper, we present a novel example selective and order independent method for learning-based image super-resolution. We first present a method that selectively utilizes training samples according to the content of the input image. Experimental results show that by selecting the training samples appropriately, it is possible to dramatically reduce the computational costs without degrading image quality. We then present a new order independent technique that is shown to perform better than traditional order dependent techniques in learning image super-resolution and can also be applied to image editing such as region filling and object removal from images.
Keywords: Image resolution
Image sampling
Learning (artificial intelligence)
Publisher: IEEE
ISBN: 0-7803-9266-3
DOI: 10.1109/ISPACS.2005.1595350
Appears in Collections:Conference Paper

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