Kervrann Charles, Boulanger Jérôme
IRISA-INRIA Rennes/INRA MIA, France.
IEEE Trans Image Process. 2006 Oct;15(10):2866-78. doi: 10.1109/tip.2006.877529.
A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods.
提出了一种新颖的基于自适应和补丁的图像去噪与表示方法。该方法基于在每个像素的可变邻域中逐点选择固定大小的小图像补丁。我们的贡献在于以一种在每个空间位置平衡近似精度和随机误差的方式，将自适应邻域内数据点的加权和与每个像素相关联。该方法具有通用性，在点的局部邻域存在重复模式的假设下即可应用。通过引入空间适应性，我们扩展了Buades等人先前描述的工作，该工作可视为双边滤波到图像补丁的扩展。最后，我们提出了一种几乎无参数的图像去噪算法。该方法应用于人工 corrupted（白色高斯噪声）图像和真实图像，其性能非常接近甚至在某些情况下超过已发表的去噪方法。
