Manifold Learning for Image Denoising
Manifold Learning for Image Denoising
复制标题
DOI：
10.1109/cit.2005.139
复制
发表时间：
2005-09
期刊：
影响因子：
--
通讯作者：
Rongjie Shi;I-Fan Shen;Wenbin Chen;Su Yang
中科院分区：
文献类型：
--
作者：
Rongjie Shi;I-Fan Shen;Wenbin Chen;Su Yang
提出了一种新的图像去噪方法。尽管最近开发的双密度离散小波变换（双密度DWT）的复杂性，它仍然产生文物或破坏模糊的数据的精细结构。受最近的流形学习方法，特别是局部线性嵌入（LLE）的启发，我们发现了一个潜在的事实，即噪声和去噪图像中的图像补丁在这两个不同的空间中构建了具有相似局部几何形状的流形。因此，我们通过测量由特征向量表示的图像块如何在特征空间中由其最近邻居重建来表征局部几何。除了使用训练图像块来构造嵌入之外，我们还提出了重叠目标去噪图像块以满足局部相容性和平滑性约束。在我们的方法中，双密度DWT与LLE结合用于去噪。实验结果表明，该方法对噪声类型具有灵活性，在保持图像精细结构方面达到了最先进的性能
This paper presents a novel scheme for image denoising. In spite of the sophistication of recently developed double-density discrete wavelet transforms (double-density DWTs), it still produces artifacts or destroys fine structures by blurring the data. Inspired by recent manifold learning methods, especially the locally linear embedding (LLE), we discover the underlying fact that image patches in noisy and denoised images construct manifolds with similar local geometry in these two distinct spaces. Therefore, we characterize local geometry by measuring how an image patch represented by a feature vector can be reconstructed by its nearest neighbors in feature space. Besides using the training image patches to construct the embedding, we also propose to overlap the target denoised image patches to satisfy local compatibility and smoothness constraints. In our method, double-density DWTs is incorporated with LLE for the purpose of denoising. The experimental results show that our method is flexible with noise type and achieves state-of-the-art performance particularly in terms of preserving the fine structures