Edge preserving suppression for depth estimation via comparative variation. Issue 5 (1st May 2018)

• Record Type: Journal Article
• Authors: Baek, Eu‐Tteum; Ho, Yo‐Sung
• Is Part Of: IET image processing. Volume 12:Issue 5(2018)
• Page Start: 629
• Page End: 636
• Publication Date: 2018-05-01
• Subjects: least squares approximations -- iterative methods -- image colour analysis -- image texture -- image denoising -- computer vision; depth estimation -- comparative variation title -- computer vision -- texture suppression -- noise suppression -- colour intensity variation -- texture patterns -- edge‐preserving suppression method -- functional energy function -- relative total intensity -- space variation -- iteratively reweighted least squares -- colour image -- depth map -- depth acquisition method; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0506
• Languages: English
• ISSNs: 1751-9659

Abstract

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.