A New Image Denoising Algorithm via Bivariate Shrinkage Based on Quaternion Wavelet Transform

Denoising of images corrupted by additive white Gaussian noise is a classical problem in image processing. This paper proposes an efficient algorithm for removing Gaussian noise from corrupted image by using quaternion wavelet transform and bivariate shrinkage function. The image is decomposed by quaternion wavelet transform to obtain the quaternion coefficients in each sub-band. Then a bivariate shrinkage function filter by using the maximum a posteriori is used to model the statistical dependencies among intra-scale quaternion coefficients. The proposed algorithm is compared with other denoising techniques in terms of PSNR. The experimental results indicate that the proposed algorithm outperforms the other denoising algorithms significantly.

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