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Image denoising via sparse representation using rotational dictionary

机译:使用旋转字典通过稀疏表示对图像进行去噪

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A dictionary-learning-based image denoising algorithm is proposed in this paper. Since traditional methods seldom take into account the rotational invariance of dictionaries learned from image patches, an improved K-means singular value decomposition algorithm is developed. In our method, the rotational version of atoms is introduced to greedily match the noisy image in a sparse coding procedure. On the other hand, in a dictionary learning procedure, to maximize the diversity of atoms, a rotational operation on the residual error is adopted such that the rotational correlation among atoms is reduced. As the strategy exploits the rotational invariance of atoms, more intrinsic features existing in image patches can be effectively extracted. Experiments illustrate that the proposed method can achieve a better performance than some other well-developed denoising methods. (C) 2014 SPIE and IS&T
机译:提出了一种基于字典学习的图像去噪算法。由于传统方法很少考虑从图像补丁中学习到的字典的旋转不变性,因此开发了一种改进的K均值奇异值分解算法。在我们的方法中,引入原子的旋转形式以稀疏编码过程贪婪地匹配噪声图像。另一方面,在字典学习过程中,为了使原子的多样性最大化,采用了对残余误差的旋转操作,从而减小了原子之间的旋转相关性。由于该策略利用了原子的旋转不变性,因此可以有效地提取图像块中存在的更多固有特征。实验表明,与其他一些完善的去噪方法相比,该方法具有更好的性能。 (C)2014 SPIE和IS&T

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