SURF Threshold De-noising Method Based on the Contourlet Wavelet Transformation

Image noise always affects the clarity of the image and leads to blurring. However, the traditional de-noising methods cannot effectively remove the image noise especially the noise in regions contains rich detail information. In order to eliminate the noise, preserve better detail information of the images and enhance the de-noising effect of Contourlet wavelet transformation for images and curb pseudo Gibbs phenomenon, the paper combines SURF threshold method to improve the image processing effect. This paper improves Contourlet transformation method and develops interface, which effectively integrates it with SURF threshold and ensures the practical application. The paper also designs image de-noising experiment, which compares the result of processing with only Contourlet transformation method and that with Contourlet-SURF threshold transformation method and carries out PSNR evaluation. The results prove the effectiveness of Contourlet—SURF threshold transformation method’s effectiveness, with pseudo Gibbs phenomenon being better curbed.