拉普拉斯矩阵
切割
拉普拉斯算子
局部一致性
分段
人工智能
阈值
图形
数学
计算机科学
图像渐变
拉普拉斯平滑
算法
计算机视觉
图像(数学)
边缘检测
图像处理
图像分割
理论计算机科学
约束满足
有限元法
数学分析
物理
热力学
网格生成
概率逻辑
作者
Xianming Liu,Deming Zhai,Rong Chen,Xiangyang Ji,Debin Zhao,Wen Gao
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2019-04-01
卷期号:28 (4): 1636-1645
被引量:52
标识
DOI:10.1109/tip.2018.2875506
摘要
Depth information is being widely used in many real-world applications. However, due to the limitation of depth sensing technology, the captured depth map in practice usually has much lower resolution than that of color image counterpart. In this paper, we propose to combine the internal smoothness prior and external gradient consistency constraint in graph domain for depth super-resolution. On one hand, a new graph Laplacian regularizer is proposed to preserve the inherent piecewise smooth characteristic of depth, which has desirable filtering properties. A specific weight matrix of the respect graph is defined to make full use of information of both depth and the corresponding guidance image. On the other hand, inspired by an observation that the gradient of depth is small except at edge separating regions, we introduce a graph gradient consistency constraint to enforce that the graph gradient of depth is close to the thresholded gradient of guidance. We reinterpret the gradient thresholding model as variational optimization with sparsity constraint. In this way, we remedy the problem of structure discrepancy between depth and guidance. Finally, the internal and external regularizations are casted into a unified optimization framework, which can be efficiently addressed by ADMM. Experimental results demonstrate that our method outperforms the state-of-the-art with respect to both objective and subjective quality evaluations.
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