Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I

Research Article

Contourlet Based Image Denoising Method Combined Recursive Cycle-Spinning Algorithm

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  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_44,
        author={Hongda Fan and Xufen Xie and Yuncui Zhang and Nianyu Zou},
        title={Contourlet Based Image Denoising Method Combined Recursive Cycle-Spinning Algorithm},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={Contourlet transform Recursive cycle-spinning Image denoising},
        doi={10.1007/978-3-319-73564-1_44}
    }
    
  • Hongda Fan
    Xufen Xie
    Yuncui Zhang
    Nianyu Zou
    Year: 2018
    Contourlet Based Image Denoising Method Combined Recursive Cycle-Spinning Algorithm
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_44
Hongda Fan1, Xufen Xie1,*, Yuncui Zhang1, Nianyu Zou1
  • 1: Dalian Polytechnic University
*Contact email: xiexf@dlpu.edu.cn

Abstract

Contourlet transform lacks shift invariance, and threshold processing on the coefficients may produce pseudo Gibbs phenomena. For recursive cycle spinning algorithm can reduce the pseudo Gibbs phenomena. This paper studies the image denoising method combined with Contourlet transform and recursive cycles pinning algorithm, The analysis show that the factor need to be adjusted. When the adjustment factor takes best value, the corresponding image objective index PSNR (Peak Signal to Noise Ratio) is the largest, and images visual effects are optimal. The experimental results show that: compared with original algorithm, changing adjustment factor, the PSNR of denoised image can be improved 0.6–1.2.