Depth image denoising is increasingly becoming the hot research topic nowadays, because it reflects the three-dimensional scene and can be applied in various fields of computer vision. But the depth images obtained from depth camera usually contain stains such as noise, which greatly impairs the performance of depth-related applications. In this article, considering that group-based image restoration methods are more effective in gathering the similarity among patches, a group-based nuclear norm and learning graph (GNNLG) model was proposed. For each patch, we find and group the most similar patches within a searching window. The intrinsic low-rank property of the grouped patches is exploited in our model. In addition, we studied the manifold learning method and devised an effective optimized learning strategy to obtain the graph Laplacian matrix, which reflects the topological structure of image, to further impose the smoothing priors to the denoised depth image. To achieve fast speed and high convergence, the alternating direction method of multipliers is proposed to solve our GNNLG. The experimental results show that the proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.

中文翻译：

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使用核范数和学习图模型的深度图像去噪

深度图像去噪正日益成为当今研究的热点，因为它反映了三维场景，可应用于计算机视觉的各个领域。但是从深度相机获得的深度图像通常包含噪声等污点，这极大地影响了与深度相关的应用程序的性能。在本文中，考虑到基于组的图像恢复方法在收集块之间的相似性方面更有效，提出了一种基于组的核范数和学习图（GNNLG）模型。对于每个补丁，我们在搜索窗口中查找并分组最相似的补丁。在我们的模型中利用了分组补丁的内在低秩属性。此外，我们研究了流形学习方法并设计了一种有效的优化学习策略来获得图拉普拉斯矩阵，它反映了图像的拓扑结构，进一步对去噪深度图像施加平滑先验。为了实现快速和高收敛，提出了乘法器的交替方向方法来解决我们的 GNNLG。实验结果表明，该方法在主观和客观标准上均优于其他当前最先进的去噪方法。