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Depth Image Denoising Using Nuclear Norm and Learning Graph Model

机译:利用核规范和学习图模型的深度图像去噪

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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.
机译:深度图像去噪越来越多地成为现在的热门研究主题,因为它反映了三维场景,可以应用于各种电脑视野的领域。但是从深度相机获得的深度图像通常包含诸如噪声的污渍,这大大损害了与深度相关的应用的性能。在本文中,考虑到基于组的图像恢复方法在收集斑块之间的相似性方面更有效地,提出了基于基于基于核规范和学习图(GNNLG)模型。对于每个补丁,我们发现并在搜索窗口中找到最相似的补丁。在我们的模型中利用分组补丁的内在低级别属性。此外,我们研究了歧管学习方法,并设计了有效优化的学习策略,以获得反映图像的拓扑结构的图拉普拉斯矩阵,进一步将平滑的前沿施加到去噪到的深度图像。为了实现快速速度和高收敛,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,该方法在主体和客观标准中优于其他目前最先进的去噪方法。

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