论文标题
较少的人工制品的图像denoing：在快速贴片重新排序上进行新颖的非线性过滤
Image denoising with less artefacts: Novel non-linear filtering on fast patch reorderings
论文作者
论文摘要
领先的denoising方法（例如3D块匹配（BM3D））基于补丁。但是，它们可能会遭受频域伪像，并需要指定明确的噪声模型。我们提出了一种基于补丁的方法，该方法避免了这些缺点。它结合了简单而快速的补丁重新排序和非线性平滑。平滑度以多种方式奖励补丁和像素相似性。我们对具有添加性白色高斯噪声（AWGN）的现实世界图像进行实验，并在具有更通用的加性噪声模型的电子显微镜数据上进行实验。我们的过滤器在77％的实验中的表现优于BM3D，相对于平方误差，高达29％的提高。
Leading denoising methods such as 3D block matching (BM3D) are patch-based. However, they can suffer from frequency domain artefacts and require to specify explicit noise models. We present a patch-based method that avoids these drawbacks. It combines a simple and fast patch reordering with a non-linear smoothing. The smoothing rewards both patch and pixel similarities in a multiplicative way. We perform experiments on real world images with additive white Gaussian noise (AWGN), and on electron microscopy data with a more general additive noise model. Our filter outperforms BM3D in 77% of the experiments, with improvements of up to 29% with respect to the mean squared error.
