Image denoising using sparse representation classification and non-subsampled shearlet transform

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• 作者：Hamid Reza Shahdoosti ; Omid Khayat
• 刊名：Signal, Image and Video Processing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：10
• 期：6
• 页码：1081-1087
• 全文大小：1,485 KB
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：10

文摘

In this paper, an image denoising method is proposed which uses sparse un-mixing by variable splitting and augmented Lagrangian (SUnSAL) classifier in the non-subsampled shearlet transform (NSST) domain. To this aim, the noisy image is decomposed into various scales and directional components using the NSST and then the feature vector for a pixel is constituted by the spatial regularity in the NSST domain. Subsequently, the NSST detail coefficients are labeled as edge-related coefficients or noise-related ones by using the SUnSAL classifier. The noisy coefficients of the NSST subbands are then denoised by the shrink method, which uses the adaptive Bayesian threshold for denoising. Finally, the inverse NSST transform is applied to the denoised coefficients. Our experiments demonstrate that the proposed approach improves the image quality in terms of both subjective and objective inspections, compared with some other state-of-the-art denoising techniques.KeywordsImage denoisingSparse representationNon-subsampled shearlet transformAdaptive Bayesian threshold