Zuo Yifan, Wu Qiang, Zhang Jian, An Ping
IEEE Trans Image Process. 2018 Aug;27(8):4145-4159. doi: 10.1109/TIP.2018.2828335. Epub 2018 Apr 18.
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.
基于马尔可夫随机场（MRF）的引导式深度图增强通常假设彩色图像与相应深度图之间存在边缘一致性。在此假设下，低质量的深度边缘可根据高质量彩色图像的引导进行细化。然而，这种一致性并非总是成立，这会导致纹理复制伪影和深度边缘模糊。此外，先前基于MRF的模型总是通过一种非结构化方案在正则化项中计算引导亲和力，该方案忽略了深度图上的局部结构。本文提出了一种新颖的基于MRF的方法。它通过由最小生成树（森林）组成的空间中像素之间的距离来计算这些亲和力，以更好地保留深度边缘。此外，在每个最小生成树内部，基于显式边缘不一致测量模型计算边的权重，这显著减轻了纹理复制伪影。为了进一步容忍噪声引起的影响并更好地保留深度边缘，提出了一种带宽自适应方案。我们的方法针对合成和真实数据集（包括Middlebury、ToF-Mark和NYU）上的深度图超分辨率和深度图完成问题进行了评估。与16种最新方法进行了全面比较。定性和定量评估均显示了性能的提升。
