Weighted Joint Sparse Representation for Removing Mixed Noise in Image
Weighted Joint Sparse Representation for Removing Mixed Noise in Image
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DOI：
10.1109/tcyb.2016.2521428
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发表时间：
2017-03
影响因子：
11.8
通讯作者：
Licheng Liu;Long Chen;C. L. P. Chen;Yuanyan Tang;Chi-Man Pun
中科院分区：
文献类型：
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作者：
Licheng Liu;Long Chen;C. L. P. Chen;Yuanyan Tang;Chi-Man Pun
联合稀疏表示（JSR）在图像处理和计算机视觉领域显示出巨大的应用潜力。然而，传统的JSR对于离群值是脆弱的。在本文中，我们提出了一种加权JSR（WJSR）模型，用于同时编码一组来自同一子空间但被噪声和离群值破坏的数据样本。我们的模型是可取的，利用这些数据样本共享的共同信息，同时减少离群值的影响。为了求解WJSR模型，我们进一步引入了一种称为加权同时正交匹配追踪的贪婪算法来有效地逼近全局最优解。然后，我们应用WJSR的混合噪声去除联合编码的分组非局部相似的图像补丁。通过将其与全局先验和稀疏误差合并到一个统一的框架中，进一步提高了去噪性能。实验结果表明，我们的去噪方法是上级优于几个国家的最先进的混合噪声去除方法。
Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and outliers. Our model is desirable to exploit the common information shared by these data samples while reducing the influence of outliers. To solve the WJSR model, we further introduce a greedy algorithm called weighted simultaneous orthogonal matching pursuit to efficiently approximate the global optimal solution. Then, we apply the WJSR for mixed noise removal by jointly coding the grouped nonlocal similar image patches. The denoising performance is further improved by incorporating it with the global prior and the sparse errors into a unified framework. Experimental results show that our denoising method is superior to several state-of-the-art mixed noise removal methods.