Contourlet transform as an effective method for agricultural product image denoising

Image denoising for agricultural product image is a one of the most basic and important step in agricultural image processing. Wavelet transform has the weakness of isotropy, which limits its use in image denoising. To solve this problem, a new image denoising algorithm based on Contourlet transform is presented. The algorithm fully utilized the advantages of Contourlet transform such as flexible multi-resolution, anisotropy and a sparse representation. In the first step, the image is decomposed by PDFB (pyramidal directional filter bank), and in the second step, the muti-scale threshold shrinkage algorithm is presented to remove the noise in high frequency sub-band, in the last step, inverse transformation of Contourlet is used and the agricultural product image denoising is realized. In order to test the performance of Contourlet denoising algorithm, a comparative test is made by using Wavelet, median filter, mean filter, Gaussian Filter and Wiener filtering methods. Results show that Contourlet denoising algorithm is suitable for agricultural product images and it also has the advantage of PSNR (higher peak signal to noise ratio) and visual effect. The algorithm proposed is practical and valid for agricultural product image denosing.