Depth map inpainting and super-resolution based on internal statistics of geometry and appearance
Depth map inpainting and super-resolution based on internal statistics of geometry and appearance
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DOI：
10.1109/icip.2013.6738194
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Satoshi Ikehata;Ji-Ho Cho;K. Aizawa
中科院分区：
文献类型：
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作者：
Satoshi Ikehata;Ji-Ho Cho;K. Aizawa
由于场景的低反射率和遮挡，多个传感器捕获的深度图往往存在分辨率低和像素缺失的问题。为了解决这些问题，我们提出了一种基于面片修复和超分辨率相结合的框架。与以往仅依赖深度信息的作品不同，我们显式地利用了捕捉相同场景的深度图和注册的高分辨率纹理图像的内部统计信息。我们考虑这些统计量来定位用于空洞填充的非局部块，并约束基于稀疏编码的超分辨率问题。执行了广泛的评估，并显示了使用真实世界数据集时的最先进性能。
Depth maps captured by multiple sensors often suffer from poor resolution and missing pixels caused by low reflectivity and occlusions in the scene. To address these problems, we propose a combined framework of patch-based inpainting and super-resolution. Unlike previous works, which relied solely on depth information, we explicitly take advantage of the internal statistics of a depth map and a registered highresolution texture image that capture the same scene. We account these statistics to locate non-local patches for hole filling and constrain the sparse coding-based super-resolution problem. Extensive evaluations are performed and show the state-of-the-art performance when using real-world datasets.