Explicit Edge Inconsistency Evaluation Model for Color-Guided Depth Map Enhancement
Explicit Edge Inconsistency Evaluation Model for Color-Guided Depth Map Enhancement
复制标题
用于颜色引导深度图增强的显式边缘不一致性评估模型
DOI：
10.1109/tcsvt.2016.2609438
复制
发表时间：
2018-02
期刊：
影响因子：
--
通讯作者：
Ping An
中科院分区：
文献类型：
--
作者：
Yifan Zuo;Qiang Wu;Jian Zhang;Ping An
颜色引导深度增强是根据深度边缘和相应位置的颜色边缘一致的假设，对深度图进行细化。在这种低级视觉任务的方法中，马尔可夫随机场（MRF）及其变体是多年来主导该领域的主要方法之一。然而，上述假设并不总是正确的。为了解决这一问题，目前的解决方法是在MRF模型的平滑项内调整加权系数。这些方法缺乏明确的评价模型来定量衡量深度边缘图与颜色边缘图的不一致性，因此无法自适应地控制来自彩色图像的引导对深度增强的努力，从而导致纹理复制伪影和深度边缘模糊等各种缺陷。在本文中，我们提出了对这种不一致性的定量度量，并明确地将其嵌入到平滑项中。与Middlebury ToF-Mark和NYU数据集上的基准和最新方法相比，所提出的方法显示了有希望的实验结果。
Color-guided depth enhancement is used to refine depth maps according to the assumption that the depth edges and the color edges at the corresponding locations are consistent. In methods on such low-level vision tasks, the Markov random field (MRF), including its variants, is one of the major approaches that have dominated this area for several years. However, the assumption above is not always true. To tackle the problem, the state-of-the-art solutions are to adjust the weighting coefficient inside the smoothness term of the MRF model. These methods lack an explicit evaluation model to quantitatively measure the inconsistency between the depth edge map and the color edge map, so they cannot adaptively control the efforts of the guidance from the color image for depth enhancement, leading to various defects such as texture-copy artifacts and blurring depth edges. In this paper, we propose a quantitative measurement on such inconsistency and explicitly embed it into the smoothness term. The proposed method demonstrates promising experimental results compared with the benchmark and state-of-the-art methods on the Middlebury ToF-Mark, and NYU data sets.
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DOI：
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发表时间：
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期刊：
2013 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
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期刊：
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影响因子：
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DOI：
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发表时间：
2010-09
影响因子：
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