School of Computer Science and Engineering, Korea University of Technology and Education, Cheonan, Korea.
IEEE Trans Image Process. 2012 May;21(5):2866-73. doi: 10.1109/TIP.2012.2186144. Epub 2012 Jan 26.
Mostly, shape-from-focus algorithms use local averaging using a fixed rectangle window to enhance the initial focus volume. In this linear filtering, the window size affects the accuracy of the depth map. A small window is unable to suppress the noise properly, whereas a large window oversmoothes the object shape. Moreover, the use of any window size smoothes focus values uniformly. Consequently, an erroneous depth map is obtained. In this paper, we suggest the use of iterative 3-D anisotropic nonlinear diffusion filtering (ANDF) to enhance the image focus volume. In contrast to linear filtering, ANDF utilizes the local structure of the focus values to suppress the noise while preserving edges. The proposed scheme is tested using image sequences of synthetic and real objects, and results have demonstrated its effectiveness.
大多数情况下，聚焦算法使用固定矩形窗口进行局部平均，以增强初始聚焦体积。在这种线性滤波中，窗口大小会影响深度图的准确性。小窗口无法正确抑制噪声，而大窗口则会过度平滑物体形状。此外，使用任何窗口大小都会均匀地平滑聚焦值。因此，会得到错误的深度图。在本文中，我们建议使用迭代三维各向异性非线性扩散滤波（ANDF）来增强图像聚焦体积。与线性滤波不同，ANDF 利用聚焦值的局部结构来抑制噪声，同时保留边缘。使用合成和真实物体的图像序列对所提出的方案进行了测试，结果表明了其有效性。
