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4 November 2014 An example image super-resolution algorithm based on modified k-means with hybrid particle swarm optimization
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Abstract
This paper presents a novel example-based super-resolution (SR) algorithm with improved k-means cluster. In this algorithm, genetic k-means (GKM) with hybrid particle swarm optimization (HPSO) is employed to improve the reconstruction of high-resolution (HR) images, and a pre-processing of classification in frequency is used to accelerate the procedure. Self-redundancy across different scales of a natural image is also utilized to build attached training set to expand example-based information. Meanwhile, a reconstruction algorithm based on hybrid supervise locally linear embedding (HSLLE) is proposed which uses training sets, high-resolution images and self-redundancy across different scales of a natural image. Experimental results show that patches are classified rapidly in training set processing session and the runtime of reconstruction is half of traditional algorithm at least in super-resolution session. And clustering and attached training set lead to a better recovery of low-resolution (LR) image.
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Kunpeng Feng, Tong Zhou, Jiwen Cui, and Jiubin Tan "An example image super-resolution algorithm based on modified k-means with hybrid particle swarm optimization", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92731I (4 November 2014); https://doi.org/10.1117/12.2073216
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