Paper Details

A novel technique capable of removing Gaussian noise with less computational complexity has beenpresented. This paper proposes a hybrid filter which employs Wavelet Transforms, a very powerful multiresolution tool,employing modified Bayes thresholding, in conjunction with the Wiener Filter. The Wiener filter tries to build an optimalestimate of the original image by enforcing a minimum mean-square error constraint between estimate and originalimage. In the first step, Discrete Wavelet transform is applied to the given image, using modified Bayes thresholding forbetter performance. This is followed by application of Wiener Filter to the output obtained in the previous stage. Theproposed algorithm is tested on a number of benchmark images and is found to produce better results in terms of thequalitative and quantitative measures of the image for both low and high values of noise variance in comparison to manyexisting techniques. The proposed technique removes Gaussian noise and the edges are better preserved with lesscomputational complexity and this aspect makes it easy to implement in hardware.

Sandip Mehta, “Weighted Hybrid Wavelet Wiener Filter for Gaussian Noise Removal”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 10, pp. 645-649, October 2017.