Depth Map Recovery Based on a Unified Depth Boundary Distortion Model

Wang, Haotian; Yang, Meng; Lan, Xuguang; Zhu, Ce; Zheng, Nanning

doi:10.1109/tip.2022.3216768

Cited by 11 publications

(1 citation statement)

References 56 publications

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“…Jung et al [13] propose a post-processing algorithm to refine the depth image using super-pixel segmentation and considering the relation between multiple views. Wang et al [14] propose an RGB-guided depth image recovery method to recover true boundaries in seriously distorted depth images. The optimization-based methods generally use the depth image degradation model and various prior knowledge to transform the reconstruction problem into the cost function optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Improved Upsampling Based Depth Image Super-Resolution Reconstruction

Zhou

Wang

et al. 2023

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 99%