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Improved Upsampling Based Depth Image Super-Resolution Reconstruction
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DOI：10.1109/ACCESS.2023.3274966.png)
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Abstract
En 中文
Constrained by current sensing technology, depth camera only acquires a low-resolution depth image that does not meet actual requirements. To solve this problem, this paper take a divide-and-conquer strategy to synthesize a high-resolution depth image from a low-resolution range image under the guidance of a registered high-resolution color image. Initially, the depth image is divided into planar areas and edge regions. For different zones, we exploit different methods to interpolate the missing depths. At planar area, the linear interpolation method is employed to perform upsampling. At edge region, a segmentation-separation upsampling method is used to interpolate the missing values. Then the upsampling result are refined on the Depth CNN that is built in this paper. We conduct extensive experiments on the benchmark database and real world data with various upsampling rates to illustrate the upsampling ability of our method. The comparison with classical super-resolution algorithms demonstrates that our upsampling algorithm achieves the best quality with fewer artifacts and our depth CNN outperforms the most state-of-the-art methods in terms of qualitative and quantitative evaluations.
Keywords:
Image edge detection
Image resolution
Optical filters
Image reconstruction
Image color analysis
Interpolation
Image segmentation
Depth image
upsampling
super-resolution reconstruction
edge guided
Journal
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3.6
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9.8W
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29.4W