The image denoising model based on non-local self-similarity prior (NSS) has received extensive attention in recent years because of the repeated structure of natural image patches. Similar patches collected by exploiting NSS prior are sparse, which can be used to estimate potential lowrank subspace. Meanwhile, the modelling of natural images, such as Gaussian mixture models (GMMs), has been successful in all aspects of computer vision by reducing the patterns of image patches. However, the version of its geometric transformation (e.g. rotational transformation) cannot be matched directly by using distance. How to further reduce the patterns of the patches by geometric prior and accelerate rotational matching through parallel calculation is an issue that needs to be solved. In this study, an external guided rotational matching denoising framework is proposed. The proposed framework combines non-local, sparse and low-rank image priors and we design a parallel computing scheme. They demonstrate the performance improvement of the proposed algorithm on images with strong rotational properties and the comparison with traditional state-of-the-art denoising methods. The scalability and effectiveness of the new framework are verified by simulation experiments in public and real datasets.

中文翻译：

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基于外部先验引导式旋转聚类的去噪框架

近年来，由于自然图像块的重复结构，基于非局部自相似先验（NSS）的图像去噪模型受到了广泛关注。通过先验利用NSS收集的相似补丁稀疏，可用于估计潜在的低秩子空间。同时，通过减少图像补丁的模式，自然图像的建模（例如高斯混合模型（GMM））已在计算机视觉的各个方面取得了成功。但是，其几何变换（例如旋转变换）的版本无法通过使用距离直接匹配。如何通过几何先验进一步减小斑块的图案，以及如何通过并行计算来加速旋转匹配是需要解决的问题。在这个研究中，提出了一种外部引导旋转匹配降噪框架。所提出的框架结合了非本地，稀疏和低秩图像先验，并且我们设计了一种并行计算方案。他们证明了所提算法在具有强旋转特性的图像上的性能改进，并与传统的最新去噪方法进行了比较。新框架的可扩展性和有效性通过公共和真实数据集中的仿真实验得到了验证。