Low-rank restoration has recently attracted a lot of attention in the research of computer vision. Empirical studies show that exploring the low-rank property of the patch groups can lead to superior restoration performance, however, there is limited achievement on the global low-rank restoration because the rank minimization at image level is too strong for the natural images which seldom match the low-rank condition. In this paper, we describe a flexible global low-rank restoration model which introduces the local statistical properties into the rank minimization. The proposed model can effectively recover the latent global low-rank structure via nuclear norm, as well as the fine details via Gaussian mixture model. An alternating scheme is developed to estimate the Gaussian parameters and the restored image, and it shows excellent convergence and stability. Besides, experiments on image and video sequence datasets show the effectiveness of the proposed method in image inpainting problems.

中文翻译：

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使用高斯混合模型的全局低秩图像恢复


低秩恢复最近在计算机视觉的研究中引起了广泛的关注。实证研究表明，探索斑块组的低秩特性可以带来优异的恢复性能，但是，由于图像级别的秩最小化对于很少出现的自然图像来说太强，因此在全局低秩恢复方面取得的成果有限。符合低等级条件。在本文中，我们描述了一种灵活的全局低秩恢复模型，它将局部统计特性引入到秩最小化中。所提出的模型可以通过核范数有效地恢复潜在的全局低秩结构，并通过高斯混合模型有效地恢复精细细节。开发了一种交替方案来估计高斯参数和恢复图像，并且表现出良好的收敛性和稳定性。此外，在图像和视频序列数据集上的实验表明了该方法在图像修复问题上的有效性。