Image is often easily polluted by noise in the process of image processing, so image de-noising is an important step in the field of image processing. Based on the wavelet threshold de-noising algorithm, an improved image de-noising algorithm based on wavelet and Wiener filter is proposed in this paper, which can effectively reduce the Gaussian white noise. Firstly we use wavelet soft threshold to reduce noise, then use Wiener filter to process the image and get the valuation of the image. Experimental results show that the proposed algorithm on image de-noising not only can effectively suppress Gaussian white noise, but also can well retain the details of image edges.
Cite this paper
Wang, Y. and Li, T. (2016). Application of Wavelet and Wiener Filtering Algorithm in Image De-Noising. Open Access Library Journal, 3, e2319. doi: http://dx.doi.org/10.4236/oalib.1102319.
Milanfar, P. (2013) A Tour of Modern Image Filtering: New Insights and Methods,
Both Practical and Theoretical. IEEE Signal
Processing Magazine, 30, 106-128. http://dx.doi.org/10.1109/MSP.2011.2179329
Ramani, S., Blu, T. and
Unser, M. (2008 ) Monte-Carlo Sure: A Black-Box Optimization of Regularization Parameters
for Generalde-Noising Algorithms. IEEE Transactions on Image
Processing, 17, 1540-1554.
Goossens, B., Pizurica, A. and
Philips, W. (2009) Image de-Noising
Using Mixtures of Projected Gaussian Scale Mixtures. IEEE Transactionson Image Processing, 18, 1689-1702.
Zhang, M. and Gunturk, B.K. (2008) Multiresolution Bilateral Filtering
for Image De-Noising. IEEE Transactions on ImageProcessing, 17, 2324-2333. http://dx.doi.org/10.1109/TIP.2008.2006658
Ju, S.G., He, K. and Zhou, J.L. (2010) Image with Gauss Noise De-Noising Based on Neighborhood
Characteristics. Journal of Sichuan
University, 42, 139-144.
Guo, S.X. and Tang, Y.J. (2008) The Popularization and Application
of Image Processing Wiener Filter. Computer Engineering and Applications, 44, 178-180.
