Abstract

In this paper, a Supervised Classification assisted Markov Random Field (SC-MRF) model is proposed for generating high-quality up-sampled depth maps. The proposed model aims to reduce depth bleeding and depth confusion artifacts that can be produced at boundary regions of the up-sampled depth maps. In the proposed model, segmentation of low-resolution (LR) depth map is first used to supervise the classification of corresponding high-resolution (HR) color image. With this supervised classification, not only can the depth edges be retained, but redundant textures in the HR color image can be omitted. The classification result is then introduced into the design of a MRF energy function, and the final up-sampled depth map is obtained by optimizing this energy function with the gradient descent algorithm. For simplicity, classical K-means clustering is adopted to segment the LR depth map into several classes, and a feature-based K-nearest neighbour (K-NN) method is utilized for the supervised classification. With the proposed SC-MRF model, interaction between depths of different classes will be strongly suppressed, meaning depth edges are well preserved. Comparisons with the state-of-the-art demonstrate the strong performance of the proposed method both visually and by quantitative evaluation.

Original languageEnglish
Pages (from-to)315-320
Number of pages6
JournalPattern Recognition Letters
Volume138
Early online date24 Jul 2020
DOIs
Publication statusPublished - 31 Oct 2020

Keywords

  • Gradient descent algorithm
  • K-means clustering
  • K-NN
  • Markov random fields
  • Supervised classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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