College of Information and Communication, Natural Sciences Campus, Sunkyunkwan University, Suwon, Republic of Korea.
Microsc Res Tech. 2021 Apr;84(4):656-667. doi: 10.1002/jemt.23623. Epub 2020 Oct 19.
Three-dimensional shape recovery is an important issue in the field of computer vision. Shape from Focus (SFF) is one of the passive techniques that uses focus information to estimate the three-dimensional shape of an object in the scene. Images are taken at multiple positions along the optical axis of the imaging device and are stored in a stack. In order to reconstruct the three dimensional shape of the object, the best-focused positions are acquired by maximizing the focus curves obtained via application of a focus measure operator. In this article, Deep Neural Network (DNN) is employed to extract the more accurate depth of each object point in the image stack. The size of each image in the stack is first reduced and then provided to the proposed DNN network to aggregate the shape. The initial shape is refined by applying a median filter, and later the reconstructed shape is sized back to original by utilizing bi-linear interpolation. The results are compared with commonly used focus measure operators by employing root mean squared error (RMSE), correlation, and image quality index (Q). Compared to other methods, the proposed SFF method using DNN shows higher precision and low computational time consumption.
三维形状恢复是计算机视觉领域的一个重要问题。聚焦法（SFF）是一种被动技术，它利用聚焦信息来估计场景中物体的三维形状。图像沿着成像设备的光轴在多个位置拍摄，并存储在一个堆栈中。为了重建物体的三维形状，通过应用聚焦度量算子来获取最佳聚焦位置，从而获得聚焦曲线。在本文中，采用深度神经网络（DNN）来提取图像堆栈中每个物体点的更准确的深度。首先缩小堆栈中每个图像的大小，然后将其提供给所提出的 DNN 网络以聚合形状。通过应用中值滤波器来细化初始形状，然后利用双线性插值将重建的形状调整回原始大小。通过均方根误差（RMSE）、相关性和图像质量指数（Q）将结果与常用的聚焦度量算子进行比较。与其他方法相比，使用 DNN 的 SFF 方法具有更高的精度和更低的计算时间消耗。
