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Title: Fast super-resolution based on weighted collaborative representation
Authors: Li, H
Lam, KM 
Issue Date: 2014
Source: 2014 19th International Conference on Digital Signal Processing : Hong Kong, 20-23 August 2014, 6900801, p. 914-918
Abstract: Recently, collaborative representation (CR) has been proposed as an l2-norm least-square solution for image super-resolution with significantly less computation than the l1-norm version of the Sparse-Coding-based Super-Resolution (ScSR) without any sacrifice in terms of image quality. In this paper we propose a novel weighted collaborative representation (WCR) instead of the original CR model for single image super-resolution. Our proposed method can achieve more than a 0.2∼0.3 dB gain without requiring any additional cost compared to the original CR model. Moreover, we devise a hierarchicalclustering KD-tree searching scheme which can reduce the computational complexity on searching part in our WCR model from O(n) to O(n1/m), where n is the atom count and m is the number of layers, without any compromise of image quality.
Keywords: Collaborative representation
Image super-resolution
Ridge regression
Sparse coding
Publisher: Institute of Electrical and Electronics Engineers Inc.
ISBN: 9781479946129
DOI: 10.1109/ICDSP.2014.6900801
Appears in Collections:Conference Paper

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