Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55571
Title: Practical application of random forests for super-resolution imaging
Authors: Huang, JJ
Siu, WC 
Keywords: Fast approach
Image processing
Learning
Random forest
Super-resolution
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - IEEE International Symposium on Circuits and Systems, ISCAS 2015, 24-27 May 2015, 7169108, p. 2161-2164 How to cite?
Abstract: In this paper, a novel learning-based single image super-resolution method using random forest is proposed. Different from example-based super-resolution methods which search for similar image patches from an external database or the input image, and the sparse representation model based methods which rely on the sparse representation, this proposed super-resolution with random forest (SRRF) method takes the divide-and-conquer strategy. Random forest is applied to classify the training LR-HR patch pairs into a number of classes. Within every class, a simple linear regression model is used to model the relationship between the LR image patches and their corresponding HR image patches. Experimental results show that the proposed SRRF method can generate the state-of-the-art super-resolved images with near real-time performance.
URI: http://hdl.handle.net/10397/55571
ISBN: 9781479983919
ISSN: 0271-4310
DOI: 10.1109/ISCAS.2015.7169108
Appears in Collections:Conference Paper

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