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.

本文提出了一种监督分类辅助马尔可夫随机场（SC-MRF）模型，用于生成高质量的上采样深度图。提出的模型旨在减少可能在上采样深度图的边界区域产生的深度渗色和深度混淆伪像。在提出的模型中，首先使用低分辨率（LR）深度图的分割来监督相应的高分辨率（HR）彩色图像的分类。通过这种监督分类，不仅可以保留深度边缘，而且可以忽略HR彩色图像中的多余纹理。然后将分类结果引入MRF能量函数的设计中，并通过使用梯度下降算法对该能量函数进行优化来获得最终的上采样深度图。为了简单起见，古典采用K- means聚类将LR深度图划分为几类，并采用基于特征的K-最近邻（K- NN）方法进行监督分类。使用提出的SC-MRF模型，将大大抑制不同类别​​的深度之间的相互作用，这意味着深度边缘得到了很好的保留。与最新技术的比较从视觉上和通过定量评估证明了所提出方法的强大性能。