The standard separable two dimensional wavelet transform has achieved a great success in image
denoising applications due to its sparse representation of images. However it fails to capture efficiently the
anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis
functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi
directional and anisotropic wavelet transform called directionlet is presented. The image denoising in
wavelet domain has been extended to the directionlet domain to make the image features to concentrate on
fewer coefficients so that more effective thresholding is possible. The image is first segmented and the
dominant direction of each segment is identified to make a directional map. Then according to the
directional map, the directionlet transform is taken along the dominant direction of the selected segment.
The decomposed images with directional energy are used for scale dependent subband adaptive optimal
threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL
subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input
to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the
proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and
visual quality
Description:
Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.6, December 2012