An improved image denoising method based on contourlet transform and NeighShrink algorithm Online publication date: Thu, 10-May-2018
by Jian Liu; Tong Li; Ke Xu; Song Bo Wei; Ling Chang
International Journal of Computer Applications in Technology (IJCAT), Vol. 57, No. 2, 2018
Abstract: It is important how to denoising while using images for non-destructive detection. In order to effectively remove image noise and preserve the good image detail, in this paper, a new improved image denoising method has been proposed based on Contourlet and NeighShrink. Firstly, the image of the Contourlet coefficients is obtained by the Contourlet transform. And then, the Contourlet coefficients are contracted by the neighbourhood shrinkage method. Lastly, the image is denoised by the inverse Contourlet transform. Taking elevator fault detection as a practical application example in the simulation study, the images of the elevator machine and the wire rope are denoised after greying. The detection images of traction motor and wire rope are analysed and compared with the different denoising method. The simulation experiments show that the improved algorithm can better protect the image details of the elevator machine and the wire rope, avoiding the Gibbs phenomenon.
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