Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus images, and the quality of reconstructed shape mainly depends on the accuracy of image focus volume (FV). Traditional SFF techniques exhibit poor performance in preserving structural edges and fine details while removing noisy artifacts, and mostly they do not incorporate any additional shape prior. Therefore, in this paper, we propose to refine FV by formulating an energy minimization framework that employs a nonconvex regularizer and incorporates two types of shape priors. The proposed regularizer is robust against noisy focus values. The first proposed shape prior is input image sequence and it is a single and static shape prior. While, the second shape prior corresponds to a series of shape priors. These shape priors are FVs which are iteratively obtained on-the-fly. Both of these shape priors constrain the solution space for output FV. We optimize nonconvex energy function through majorize-minimization algorithm which iteratively guarantees a local minimum and converges quickly. Experiments have been conducted to evaluate accuracy and convergence properties of the proposed method. Experimental results of synthetic and real image sequences demonstrate that our method achieves superior results in terms of ability to reconstruct accurate 3D shapes as compared to existing approaches.

中文翻译：

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焦点形状中的鲁棒焦点体积正则化


焦点形状（SFF）从一系列多焦点图像重建场景的 3D 形状，重建形状的质量主要取决于图像焦点体积（FV）的精度。传统的 SFF 技术在保留结构边缘和精细细节、同时去除噪声伪影方面表现不佳，而且大多数情况下它们不会先合并任何额外的形状。因此，在本文中，我们建议通过制定能量最小化框架来细化 FV，该框架采用非凸正则化器并结合两种类型的形状先验。所提出的正则化器对于噪声焦点值具有鲁棒性。第一个提出的形状先验是输入图像序列，它是单个静态形状先验。同时，第二形状先验对应于一系列形状先验。这些形状先验是动态迭代获得的 FV。这两个形状先验都限制了输出 FV 的解空间。我们通过极大极小化算法优化非凸能量函数，该算法迭代地保证局部最小值并快速收敛。已经进行了实验来评估所提出方法的准确性和收敛性。合成图像序列和真实图像序列的实验结果表明，与现有方法相比，我们的方法在重建准确 3D 形状的能力方面取得了优异的结果。