A weighted discriminative approach for image denoising with overcomplete representations
A weighted discriminative approach for image denoising with overcomplete representations
复制标题
具有过完备表示的图像去噪的加权判别方法
DOI：
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发表时间：
2010
期刊：
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通讯作者：
Michael Elad
中科院分区：
文献类型：
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作者：
A. Adler;Y. Hel;Michael Elad
提出了一种新的加权方法用于图像去噪中的收缩函数学习。该方法通过强调稀疏过完备表示分量的贡献，优化了收缩函数的形状，并最大限度地提高了去噪性能。与以前的工作相比，我们在过完备域中应用权重，并将恢复图像制定为收缩后过完备表示的加权组合。我们进一步在收缩函数的离线最小二乘学习阶段利用该公式，从而使其形状适应加权过程。去噪后的图像重建与学习的加权收缩函数。计算机模拟表明，上级收缩为基础的去噪性能。
We present a novel weighted approach for shrinkage functions learning in image denoising. The proposed approach optimizes the shape of the shrinkage functions and maximizes denoising performance by emphasizing the contribution of sparse overcomplete representation components. In contrast to previous work, we apply the weights in the overcomplete domain and formulate the restored image as a weighted combination of the post-shrinkage overcomplete representations. We further utilize this formulation in an offline Least Squares learning stage of the shrinkage functions, thus adapting their shape to the weighting process. The denoised image is reconstructed with the learned weighted shrinkage functions. Computer simulations demonstrate superior shrinkage-based denoising performance.