Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76551
Title: Image super-resolution via weighted random forest
Authors: Liu, ZS 
Siu, WC 
Huang, JJ 
Keywords: Image super-resolution
Learning
Random forest
Weighting
Rotation
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE International Conference on Industrial Technology (ICIT), Toronto, Canada, Mar 22-25, 2017, p. 1019-1023 How to cite?
Abstract: This paper proposes a novel learning-based image super-resolution via a weighted random forest model (SWRF). The proposed method uses the LR-HR training data to train a random forest model. The underlying idea of this approach is to use several decision trees to classify the training data based on a simple splitting threshold value at each class. A linear regression model is learnt to map the relationship between LR and HR patches. During the up-sampling process, to obtain a more robust super-resolved HR image, instead of averaging the linear regression models from different trees, a biased weighting vector is learnt to adaptively super-resolve the LR image. Furthermore, we improve this proposed image super-resolution method via a weighted random forest model with rotation (SWRF-f) to further improve the super-resolution quality. Sufficient experimental results prove that the proposed approach can achieve the state-of-the-art super-resolution performance with reduced computation time.
URI: http://hdl.handle.net/10397/76551
ISBN: 978-1-5090-5320-9
Appears in Collections:Conference Paper

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