Abstract In this paper, we address the problem of depth recovery from a sequence of multi-focus images, known as shape-from-focus (SFF). The conventional SFF techniques typically exhibit poor performance over textureless regions, and it is difficult to preserve depth edges and fine details while maintaining spatial consistency. Therefore, we propose an SFF depth recovery framework composed of depth reconstruction and refinement processes. We first formulate the depth reconstruction as a maximum a posterior (MAP) estimation problem with the inclusion of matting Laplacian prior. The nonlocal principle is adopted in matting Laplacian matrix construction to preserve depth edges and fine details. As the nonlocal principle breaks the spatial consistency, the reconstructed depth image is spatially inconsistent and suffers from the texture-copy artifacts. To smooth the noise and suppress the texture-copy artifacts, a closed-form edge-preserving depth refinement is proposed, which is formulated as a MAP estimation problem using Markov random fields (MRFs). Experimental results over synthetic and real scene datasets demonstrate the superiority of our algorithm in terms of robustness, and the ability to preserve edges and fine details while maintaining spatial consistency compared to existing approaches.

中文翻译：

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使用非局部消光拉普拉斯先验和基于 MRF 的细化的焦点形状重建

摘要在本文中，我们解决了从一系列多焦点图像中恢复深度的问题，称为焦点形状（SFF）。传统的 SFF 技术通常在无纹理区域表现出较差的性能，并且难以在保持空间一致性的同时保留深度边缘和精细细节。因此，我们提出了一个由深度重建和细化过程组成的 SFF 深度恢复框架。我们首先将深度重建公式化为最大后验 (MAP) 估计问题，其中包含消光拉普拉斯先验。在抠图拉普拉斯矩阵构造中采用非局部原理以保留深度边缘和精细细节。由于非局部原理破坏了空间一致性，重建的深度图像在空间上不一致，并且会受到纹理复制伪影的影响。为了平滑噪声并抑制纹理复制伪影，提出了一种封闭形式的边缘保留深度细化，将其表述为使用马尔可夫随机场 (MRF) 的 MAP 估计问题。在合成和真实场景数据集上的实验结果证明了我们的算法在鲁棒性方面的优越性，以及与现有方法相比，在保持空间一致性的同时保留边缘和精细细节的能力。