Depth image denoising using nuclear norm and learning graph model
- URL: http://arxiv.org/abs/2008.03741v1
- Date: Sun, 9 Aug 2020 15:12:16 GMT
- Title: Depth image denoising using nuclear norm and learning graph model
- Authors: Chenggang Yan, Zhisheng Li, Yongbing Zhang, Yutao Liu, Xiangyang Ji,
Yongdong Zhang
- Abstract summary: Group-based image restoration methods are more effective in gathering the similarity among patches.
For each patch, we find and group the most similar patches within a searching window.
The proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
- Score: 107.51199787840066
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The depth images denoising are increasingly becoming the hot research topic
nowadays because they reflect the three-dimensional (3D) 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 paper, 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 (ADMM) 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.
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