Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8113
Title: Fast image interpolation via random forests
Authors: Huang, JJ
Siu, WC 
Liu, TR
Keywords: Classification
Decision trees
Image interpolation
Image processing
Random forests
Scalable interpolation
Training and linear regression
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2015, v. 24, no. 10, p. 3232-3245 How to cite?
Journal: IEEE transactions on image processing 
Abstract: This paper proposes a two-stage framework for fast image interpolation via random forests (FIRF). The proposed FIRF method gives high accuracy, as well as requires low computation. The underlying idea of this proposed work is to apply random forests to classify the natural image patch space into numerous subspaces and learn a linear regression model for each subspace to map the low-resolution image patch to high-resolution image patch. The FIRF framework consists of two stages. Stage 1 of the framework removes most of the ringing and aliasing artifacts in the initial bicubic interpolated image, while Stage 2 further refines the Stage 1 interpolated image. By varying the number of decision trees in the random forests and the number of stages applied, the proposed FIRF method can realize computationally scalable image interpolation. Extensive experimental results show that the proposed FIRF(3, 2) method achieves more than 0.3 dB improvement in peak signal-to-noise ratio over the state-of-the-art nonlocal autoregressive modeling (NARM) method. Moreover, the proposed FIRF(1, 1) obtains similar or better results as NARM while only takes its 0.3% computational time.
URI: http://hdl.handle.net/10397/8113
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2015.2440751
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