Minimum Spanning Forest With Embedded Edge Inconsistency Measurement Model for Guided Depth Map Enhancement

马尔可夫随机场 人工智能 深度图 像素 数学 计算机视觉 最小生成树 计算机科学 算法 模式识别(心理学) 图像分割 图像(数学)
作者
Yifan Zuo,Qiang Wu,Jian Zhang,Ping An
出处
期刊:IEEE transactions on image processing [Institute of Electrical and Electronics Engineers]
卷期号:27 (8): 4145-4159 被引量:23
标识
DOI:10.1109/tip.2018.2828335
摘要

Guided depth map enhancement based on Markov Random Field (MRF) normally assumes edge consistency between the color image and the corresponding depth map. Under this assumption, the low-quality depth edges can be refined according to the guidance from the high-quality color image. However, such consistency is not always true, which leads to texture-copying artifacts and blurring depth edges. In addition, the previous MRF-based models always calculate the guidance affinities in the regularization term via a non-structural scheme which ignores the local structure on the depth map. In this paper, a novel MRF-based method is proposed. It computes these affinities via the distance between pixels in a space consisting of the Minimum Spanning Trees (Forest) to better preserve depth edges. Furthermore, inside each Minimum Spanning Tree, the weights of edges are computed based on explicit edge inconsistency measurement model, which significantly mitigates texture-copying artifacts. To further tolerate the effects caused by noise and better preserve depth edges, a bandwidth adaption scheme is proposed. Our method is evaluated for depth map super-resolution and depth map completion problems on synthetic and real datasets including Middlebury, ToF-Mark and NYU. A comprehensive comparison against 16 state-of-the-art methods is carried out. Both qualitative and quantitative evaluation present the improved performances.
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