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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2016> 25(1): 135-144
WU Yi-quan, DAI Yi-mian, WU Jian-sheng. Improved preprocessed Yaroslavsky filter based on shearlet features[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2016, 25(1): 135-144. doi: 10.15918/j.jbit1004-0579.201625.0120
Citation: WU Yi-quan, DAI Yi-mian, WU Jian-sheng. Improved preprocessed Yaroslavsky filter based on shearlet features[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2016, 25(1): 135-144.doi:10.15918/j.jbit1004-0579.201625.0120
shu

Improved preprocessed Yaroslavsky filter based on shearlet features

doi:10.15918/j.jbit1004-0579.201625.0120
  • 1.

    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China;State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China

  • 2.

    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

  • 3.

    State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China

  • Received Date:2014-05-30
    Abstract
  • An improved preprocessed Yaroslavsky filter (IPYF) is proposed to avoid the nick effects and obtain a better denoising result when the noise variance is unknown. Different from its predecessors, the similarity between two pixels is calculated by shearlet features. The feature vector consists of initial denoised results by the non-subsampled shearlet transform hard thresholding (NSST-HT) and NSST coefficients, which can help allocate the averaging weights more reasonably. With the correct estimated noise variance, the NSST-HT can provide good denoised results as the initial estimation and high-frequency coefficients contribute large weights to preserve textures. In case of the incorrect estimated noise variance, the low-frequency coefficients will mitigate the nick effect in cartoon regions greatly, making the IPYF more robust than the original PYF. Detailed experimental results show that the IPYF is a very competitive method based on a comprehensive consideration involving peak signal to noise ratio (PSNR), computing time, visual quality and method noise.
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