Local adaptivity to variable smoothness for exemplar-based image regularization and representation
Local adaptivity to variable smoothness for exemplar-based image regularization and representation
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DOI：
10.1007/s11263-007-0096-2
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发表时间：
2008-08-01
影响因子：
19.5
通讯作者：
Boulanger, Jerome
中科院分区：
文献类型：
--
作者：
Kervrann, Charles;Boulanger, Jerome
提出了一种新型的自适应和示例性方法，用于图像恢复（DeNoing）和表示。该方法基于每个像素的可变邻域中固定大小的相似图像贴片的重点选择。主要思想是将每个像素与自适应邻域内的数据点的加权总和相关联。我们使用小型图像贴片（例如7x7或9x9贴片）来计算这些权重，因为它们能够捕获图像中看到的本地几何图案和Texel。在本文中，我们主要关注自适应邻域选择的问题，以平衡每个空间位置的近似准确性和随机误差的准确性。然后，提出的点估计器是迭代的，并自动适应了基础平滑度的程度，对要恢复的函数的先验假设最少。该方法应用于人为损坏的真实图像，性能非常接近，在某些情况下甚至超过已经发表的denoising方法的性能。提出的算法在被非高斯噪声损坏的真实图像上进行了证明，并用于生物形象中的应用。
A novel adaptive and exemplar-based approach is proposed for image restoration (denoising) and representation. The method is based on a pointwise selection of similar image patches of fixed size in the variable neighborhood of each pixel. The main idea is to associate with each pixel the weighted sum of data points within an adaptive neighborhood. We use small image patches (e.g. 7x7 or 9x9 patches) to compute these weights since they are able to capture local geometric patterns and texels seen in images. In this paper, we mainly focus on the problem of adaptive neighborhood selection in a manner that balances the accuracy of approximation and the stochastic error, at each spatial position. The proposed pointwise estimator is then iterative and automatically adapts to the degree of underlying smoothness with minimal a priori assumptions on the function to be recovered. The method is applied to artificially corrupted real images and the performance is very close, and in some cases even surpasses, to that of the already published denoising methods. The proposed algorithm is demonstrated on real images corrupted by non-Gaussian noise and is used for applications in bio-imaging.