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Title: Practical application of random forests for super-resolution imaging
Author(s): Siu, Wan Chi 
Author(s): Huang, J.-J.
Issue Date: 2015
Publisher: IEEE
Related Publication(s): Proceedings of the 2015 IEEE International Symposium on Circuits and Systems (ISCAS)
Start page: 2161
End page: 2164
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.
DOI: 10.1109/ISCAS.2015.7169108
CIHE Affiliated Publication: No
Appears in Collections:CIS Publication

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