Lee Ik-Hyun, Mahmood Muhammad Tariq, Choi Tae-Sun
1Media Lab,Massachusetts Institute of Technology,Cambridge,MA,02139USA.
2School of Computer Science and Engineering,Korea University of Technology and Education,1600 Chungjeolno,Byeogchunmyun,Cheonan,Chungnam 330-708,Republic of Korea.
Microsc Microanal. 2015 Apr;21(2):442-58. doi: 10.1017/S1431927614014597. Epub 2015 Mar 10.
Shape from focus (SFF) is a passive optical technique that reconstructs object shape from a sequence of image taken at different focus levels. In SFF techniques, computing focus measurement for each pixel in the image sequence, through a focus measure operator, is the fundamental step. Commonly used focus measure operators compute focus quality in Cartesian space and suffer from erroneous focus quality and lack in robustness. Thus, they provide erroneous depth maps. In this paper, we introduce a new focus measure operator that computes focus quality in log-polar transform (LPT) Properties of LPT, such as biological inspiration, data selection, and edge invariance, enable computation of better focus quality in the presence of noise. Moreover, instead of using a fixed patch of the image, we suggest the use of an adaptive window. The focus quality is assessed by computing variation in LPT. The effectiveness of the proposed technique is evaluated by conducting experiments using image sequences of different simulated and real objects. The comparative analysis shows that the proposed method is robust and effective in the presence of various types of noise.
聚焦形状（SFF）是一种被动光学技术，它从在不同聚焦水平拍摄的一系列图像中重建物体形状。在SFF技术中，通过聚焦度量算子计算图像序列中每个像素的聚焦测量是基本步骤。常用的聚焦度量算子在笛卡尔空间中计算聚焦质量，存在聚焦质量错误和缺乏鲁棒性的问题。因此，它们会提供错误的深度图。在本文中，我们引入了一种新的聚焦度量算子，该算子在对数极坐标变换（LPT）中计算聚焦质量。LPT的特性，如生物启发、数据选择和边缘不变性，使得在存在噪声的情况下能够计算出更好的聚焦质量。此外，我们建议使用自适应窗口，而不是使用图像的固定块。通过计算LPT中的变化来评估聚焦质量。使用不同模拟和真实物体的图像序列进行实验，评估了所提出技术的有效性。对比分析表明，所提出的方法在存在各种类型噪声的情况下具有鲁棒性和有效性。
