IEEE Trans Image Process. 2017 Apr;26(4):1732-1745. doi: 10.1109/TIP.2017.2656463. Epub 2017 Jan 20.
Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth image is much lower than that of its corresponding color image, which affects its application performance. In this paper, we propose a novel depth map super-resolution (SR) method by taking view synthesis quality into account. The proposed approach mainly includes two technical contributions. First, since the captured low-resolution (LR) depth map may be corrupted by noise and occlusion, we propose a credibility based multi-view depth maps fusion strategy, which considers the view synthesis quality and interview correlation, to refine the LR depth map. Second, we propose a view synthesis quality based trilateral depth-map up-sampling method, which considers depth smoothness, texture similarity and view synthesis quality in the up-sampling filter. Experimental results demonstrate that the proposed method outperforms state-of-the-art depth SR methods for both super-resolved depth maps and synthesized views. Furthermore, the proposed method is robust to noise and achieves promising results under noise-corruption conditions.
在许多 3D 应用中，如多视图渲染、3D 重建和 3DTV，都需要准确和高质量的深度图。然而，捕获的深度图像的分辨率远低于其对应的彩色图像，这影响了其应用性能。在本文中，我们提出了一种新的深度图超分辨率（SR）方法，考虑了视图合成质量。所提出的方法主要包括两个技术贡献。首先，由于捕获的低分辨率（LR）深度图可能受到噪声和遮挡的影响，我们提出了一种基于可信度的多视图深度图融合策略，该策略考虑了视图合成质量和采访相关性，以细化 LR 深度图。其次，我们提出了一种基于三线性的视图合成质量的深度图上采样方法，该方法在上采样滤波器中考虑了深度平滑度、纹理相似性和视图合成质量。实验结果表明，所提出的方法在超分辨深度图和合成视图方面均优于最先进的深度 SR 方法。此外，该方法对噪声具有鲁棒性，在噪声污染条件下可取得有前景的结果。
