论文标题
深度图超分辨率的多尺度渐进式融合学习
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution
论文作者
论文摘要
受深度摄像机收集的深度图的分辨率通常低于其相关的RGB摄像机的分辨率。尽管对RGB图像超分辨率(SR)进行了许多研究,但深度图超分辨率的一个主要问题是,将有明显的锯齿状边缘和细节过多的损失。为了解决这些困难,在这项工作中,我们提出了一个多尺度的渐进式融合网络,用于深度MAP SR,该网络具有渐近结构,以将分层特征整合在不同域中。考虑到低分辨率(LR)深度图及其相关的高分辨率(HR)颜色图像,我们利用两个不同的分支来实现多尺度特征学习。接下来,我们提出一种逐步融合策略来恢复人力资源深度图。最后,引入了多维损失,以限制清晰的边界和细节。广泛的实验表明,我们提出的方法在定性和定量上对最新方法产生了改进的结果。
Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera. Although there have been many researches on RGB image super-resolution (SR), a major problem with depth map super-resolution is that there will be obvious jagged edges and excessive loss of details. To tackle these difficulties, in this work, we propose a multi-scale progressive fusion network for depth map SR, which possess an asymptotic structure to integrate hierarchical features in different domains. Given a low-resolution (LR) depth map and its associated high-resolution (HR) color image, We utilize two different branches to achieve multi-scale feature learning. Next, we propose a step-wise fusion strategy to restore the HR depth map. Finally, a multi-dimensional loss is introduced to constrain clear boundaries and details. Extensive experiments show that our proposed method produces improved results against state-of-the-art methods both qualitatively and quantitatively.