期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2012
卷号：3
期号：6
页码：61
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The standard separable two dimensional wavelet transform has achieved a great success in imagedenoising applications due to its sparse representation of images. However it fails to capture efficiently theanisotropic geometric structures like edges and contours in images as they intersect too many wavelet basisfunctions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multidirectional and anisotropic wavelet transform called directionlet is presented. The image denoising inwavelet domain has been extended to the directionlet domain to make the image features to concentrate onfewer coefficients so that more effective thresholding is possible. The image is first segmented and thedominant direction of each segment is identified to make a directional map. Then according to thedirectional 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 optimalthreshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLLsubband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as inputto the inverse directionlet algorithm for getting the de-noised image. Experimental results show that theproposed method outperforms the standard wavelet-based denoising methods in terms of numeric andvisual quality.
