Image denoising via sparse and redundant representations over learned dictionaries
Image denoising via sparse and redundant representations over learned dictionaries
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DOI：
10.1109/tip.2006.881969
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发表时间：
2006-12-01
影响因子：
10.6
通讯作者：
Aharon, Michal
中科院分区：
文献类型：
--
作者：
Elad, Michael;Aharon, Michal
我们解决了图像去噪问题，其中零均值白噪声和齐次高斯加性噪声要从给定的图像中去除。所采取的方法是基于训练词典上的稀疏和冗余表示。利用K-SVD算法，我们得到了一个有效描述图像内容的词典。考虑了两种训练选项：使用受损图像本身，或在高质量图像数据库语料库上训练。由于K-SVD算法仅限于处理较小的图像块，我们将其扩展到任意图像大小，方法是预先定义一个全局图像，强制图像中每个位置的块具有稀疏性。我们展示了这种贝叶斯处理如何导致一种简单而有效的去噪算法。这导致了最先进的去噪性能，相当于，有时甚至超过了最近公布的领先的替代去噪方法。
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.