Abstract
This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling. The Minimum Mean Squared Error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new method, which can be interpreted as a generalization of the external non-local means (NLM), uses self-normalized importance sampling to efficiently approximate the MMSE estimates. The use of self-normalized importance sampling endows the proposed method with great flexibility, namely regarding the statistical properties of the measurement noise. The effectiveness of the proposed method is shown in a series of experiments using both generic large-scale and class-specific external datasets.
Abstract (translated)
本文介绍了一种基于外部数据集和重要性抽样的基于补丁的图像恢复的新方法。使用来自外部数据集的样本来近似图像块的最小均方误差（MMSE）估计，其计算需要求解多维（通常难以处理的）积分。新方法可以解释为外部非局部均值（NLM）的推广，使用自标准化重要性采样来有效地近似MMSE估计。使用自标准化重要性采样赋予所提出的方法很大的灵活性，即关于测量噪声的统计特性。在使用通用大规模和类特定外部数据集的一系列实验中显示了所提出方法的有效性。
URL
https://arxiv.org/abs/1807.03018
