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Journal of Real-Time Image Processing

, Volume 16, Issue 1, pp 115–125 | Cite as

Implementing real-time RCF-Retinex image enhancement method using CUDA

  • Xiaomin Yang
  • Lihua Jian
  • Wei Wu
  • Kai Liu
  • Binyu Yan
  • Zhili Zhou
  • Jian PengEmail author
Special Issue Paper
  • 63 Downloads

Abstract

RCF-Retinex is a novel Retinex-based image enhancement method which can improve contrast, eliminate noise, and enhance details simultaneously. It utilizes region covariance filter (RCF) to estimate the illumination. However, RCF-Retinex encounters time-consuming problem, since the region covariance filter is computationally intensive, which restricts the practical application in real-time systems. Therefore, it is necessary to decrease the computational complexity by parallelization. This paper proposes a GPU-based RCF-Retinex, which can accelerate region covariance filter using CUDA. It is feasible to use CUDA to parallel the region covariance filter due to its consecutive convolution operations, thus we can obtain the illumination image fast. Experiments have proved the improvement of running time and the enhancement results are similar with those using the unaccelerated RCF-Retinex method.

Keywords

RCF-Retinex CUDA GPU Accelerating 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiaomin Yang
    • 1
  • Lihua Jian
    • 1
  • Wei Wu
    • 1
  • Kai Liu
    • 3
  • Binyu Yan
    • 1
  • Zhili Zhou
    • 4
  • Jian Peng
    • 2
    Email author
  1. 1.College of Electronics and Information EngineeringSichuan UniversityChengduChina
  2. 2.College of Computer ScienceSichuan UniversityChengduChina
  3. 3.School of Electrical Engineering and InformationSichuan UniversityChengduChina
  4. 4.Jiangsu Engineering Center of Network Monitoring and School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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