Alexander Fractional Integral Filtering of Wavelet Coefficients for Image Denoising

The present paper, proposes an efficient denoising algorithm which works well for images corrupted with Gaussian and speckle noise. The denoising algorithm utilizes the alexander fractional integral filter which works by the construction of fractional masks window computed using alexander polynomial. Prior to the application of the designed filter, the corrupted image is decomposed using symlet wavelet from which only the horizontal, vertical and diagonal components are denoised using the alexander integral filter. Significant increase in the reconstruction quality was noticed when the approach was applied on the wavelet decomposed image rather than applying it directly on the noisy image. Quantitatively the results are evaluated using the peak signal to noise ratio (PSNR) which was 30.8059 on an average for images corrupted with Gaussian noise and 36.52 for images corrupted with speckle noise, which clearly outperforms the existing methods.