IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
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Accelerating Self-Similarity-Based Image Super-Resolution Using OpenCL

  • Jun, Jae-Hee (School of Electrical Engineering, Korea University) ;
  • Choi, Ji-Hoon (School of Electrical Engineering, Korea University) ;
  • Lee, Dae-Yeol (Electronics and Telecommunications Research Institute) ;
  • Jeong, Seyoon (Electronics and Telecommunications Research Institute) ;
  • Cho, Suk-Hee (Electronics and Telecommunications Research Institute) ;
  • Kim, Hui-Yong (Electronics and Telecommunications Research Institute) ;
  • Kim, Jong-Ok (School of Electrical Engineering, Korea University)
  • Received : 2014.04.05
  • Accepted : 2014.11.19
  • Published : 2015.02.28
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Abstract

This paper proposes the parallel implementation of a self-similarity based image SR (super-resolution) algorithm using OpenCL. The SR algorithm requires tremendous computations to search for a similar patch. This becomes a bottleneck for the real-time conversion from a FHD image to UHD. Therefore, it is imperative to accelerate the processing speed of SR algorithms. For parallelization, the SR process is divided into several kernels, and memory optimization is performed. In addition, two GPUs are used for further acceleration. The experimental results shows that a GPGPU implementation can speed up over 140 times compared to a single-core CPU. Furthermore, it was confirmed experimentally that utilizing two GPUs can speed up the execution time proportionally, up to 277 times.

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References

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  1. GPU-based real-time super-resolution system for high-quality UHD video up-conversion pp.1573-0484, 2017, https://doi.org/10.1007/s11227-017-2136-1