Depth Super-Resolution via Joint Color-Guided Internal and External Regularizations
Depth Super-Resolution via Joint Color-Guided Internal and External Regularizations
复制标题
通过联合颜色引导的内部和外部正则化实现深度超分辨率
DOI：
10.1109/tip.2018.2875506
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发表时间：
2019-04
影响因子：
10.6
通讯作者：
Gao Wen
中科院分区：
文献类型：
--
作者：
Liu Xianming;Zhai Deming;Chen Rong;Ji Xiangyang;Zhao Debin;Gao Wen
深度信息在许多实际应用中被广泛使用。然而，由于深度传感技术的限制，在实际应用中，捕获的深度图的分辨率通常比对应的彩色图像低得多。本文提出结合图域内部平滑先验和外部梯度一致性约束实现深度超分辨率。一方面，提出了一种新的图拉普拉斯正则器，保持了深度固有的分段平滑特性，具有良好的滤波性能；为了充分利用深度信息和相应的制导图像信息，定义了一个特定的权重矩阵。另一方面，由于观察到除边缘分离区域外深度梯度很小，我们引入了图梯度一致性约束，以强制图深度梯度接近制导的阈值梯度。我们将梯度阈值模型重新解释为具有稀疏性约束的变分优化。这样就解决了深度与制导结构不一致的问题。最后，将内部正则化和外部正则化整合到一个统一的优化框架中，ADMM可以有效地对其进行处理。实验结果表明，我们的方法在客观和主观质量评估方面都优于最先进的技术。
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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DOI：
10.1007/978-3-319-46487-9_44
发表时间：
2016-10
期刊：
--
影响因子：
--
作者：
Yu Li;Dongbo Min;M. Do;Jiangbo Lu
通讯作者：
Yu Li;Dongbo Min;M. Do;Jiangbo Lu
通过联合自适应正则化和流形阈值从 RGB-D 数据恢复深度
影响因子：
10.6
作者：
Liu Xianming;Zhai Deming;Chen Rong;Ji Xiangyang;Zhao Debin;Gao Wen
通讯作者：
Gao Wen
影响因子：
10.6
作者：
Xianming Liu;Debin Zhao;Ruiqin Xiong;Siwei Ma;Wen Gao
通讯作者：
Xianming Liu;Debin Zhao;Ruiqin Xiong;Siwei Ma;Wen Gao
DOI：
10.1145/1275808.1276497
发表时间：
2007-07
期刊：
ACM SIGGRAPH 2007 papers
影响因子：
--
作者：
J. Kopf;Michael F. Cohen;Dani Lischinski;M. Uyttendaele
通讯作者：
J. Kopf;Michael F. Cohen;Dani Lischinski;M. Uyttendaele
影响因子：
7.3
作者：
Alexiadis, Dimitrios S.;Zarpalas, Dimitrios;Daras, Petros
通讯作者：
Daras, Petros