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A fast deconvolution-based approach for single-image super-resolution with GPU acceleration

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Abstract

In this paper, we propose fast deconvolution-based image super-resolution (SR) with graphics processing unit (GPU)-accelerated computation. Recently, the deconvolution-based single-image SR has been proven to be very effective in upsampling images with favorable results. Based on the GPU-accelerated computation, we aim to realize the fast SR reconstruction and achieve balanceable performance in terms of both image quality and computational cost. To achieve this, we provide a novel and efficient deconvolution method to enhance the reconstruction results. We combine the gradient consistency in images with the anisotropic regularization which has been used in motion deblurring. Thus, we produce a directly parallelizable solution which is suitable for running on GPU by minimizing redundancy in computing. Experimental results demonstrate that the proposed method achieves superior performance in comparison with the existing methods with respect to image quality and runtime.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments that have led to improvements in the quality and presentation of the paper. This work was supported by the National Natural Science Foundation of China (No. 61271298) and the International S&T Cooperation Program of China (No. 2014DFG12780).

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, 2 South Taibai Road, Xi’an, 710071, China

    Cheolkon Jung, Peng Ke, Zengzeng Sun & Aiguo Gu

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  1. Cheolkon Jung
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  2. Peng Ke
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  3. Zengzeng Sun
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  4. Aiguo Gu
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Correspondence to Cheolkon Jung.

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Jung, C., Ke, P., Sun, Z. et al. A fast deconvolution-based approach for single-image super-resolution with GPU acceleration. J Real-Time Image Proc 14, 501–512 (2018). https://doi.org/10.1007/s11554-015-0513-7

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