NDLI: Edge preserving suppression for depth estimation via comparative variation

Content Provider	IET Digital Library
Author	Baek, Eu Tteum Ho, Yo Sung
Abstract	Most applications in computer vision manage to suppress textures and noise while maintaining meaningful structure based on colour intensity variation, but it is intractable due to texture patterns or error. This study presents an edge-preserving suppression method for depth estimation. The authors formulate a functional energy function based on the relative total intensity and space variation, and they minimise the energy function via iteratively reweighted least squares. Assuming that textural edges most likely correspond to depth discontinuities, they exploit the comparative variations of the colour image to produce a more accurate depth map. The experimental results demonstrate the usefulness of the proposed approach, and show that texture patterns are suppressed while meaningful edges are preserved. According to the results of the depth acquisition methods, the proposed depth estimation methods generate the accurate and robust results.
Starting Page	629
Ending Page	636
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 5, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0506
Journal	IET Image Processing
Publisher Date	2017-12-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Colour Image Colour Intensity Variation Comparative Variation Title Computer Vision Computer Vision And Image Processing Technique Depth Acquisition Method Depth Estimation Depth Map Edge-preserving Suppression Method Functional Energy Function Image Colour Analysis Image Denoising Image Texture Interpolation And Function Approximation Iterative Method Iteratively Reweighted Least Squares Least Squares Approximation Noise Suppression Numerical Analysis Optical, Image And Video Signal Processing Relative Total Intensity Space Variation Texture Pattern Texture Suppression
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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