Intensity Guided Depth Upsampling Using Edge Sparsity and Super-Weighted <inline-formula> <tex-math notation="LaTeX">$L_0$ </tex-math></inline-formula> Gradient Minimization

Intensity Guided Depth Upsampling Using Edge Sparsity and Super-Weighted <inline-formula> <tex-math notation="LaTeX">$L_0$ </tex-math></inline-formula> Gradient Minimization

Although depth cameras acquire depth in dynamic scenes, the captured depth images are often noisy and of low resolution. Depth images have the physical nature of being represented by smooth regions and edges in between them, i.e. depth images have high edge sparsity in the gradient domain. In this p...

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Bibliographic Details
Main Authors: Shengtao Yu, Hui Lan, Cheolkon Jung
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Depth upsampling
edge sparsity
intensity guided
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>₀ gradient minimization
recursive interpolation
mutual structure
Online Access:https://ieeexplore.ieee.org/document/8844650/

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https://ieeexplore.ieee.org/document/8844650/

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