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.

中文翻译：

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一种使用深度神经网络的简化方法，可实现聚焦的快速准确形状

三维形状恢复是计算机视觉领域的重要问题。聚焦形状（SFF）是一种被动技术，它使用聚焦信息来估计场景中对象的三维形状。沿成像装置的光轴在多个位置拍摄图像，并将其存储在堆栈中。为了重建对象的三维形状，通过最大化通过使用聚焦测量算子获得的聚焦曲线来获取最佳聚焦位置。在本文中，采用了深度神经网络（DNN）来提取图像堆栈中每个对象点的更准确深度。首先减少堆栈中每个图像的大小，然后将其提供给建议的DNN网络以聚合形状。初始形状可以通过应用中值滤镜进行细化，然后，利用双线性插值将重构后的形状重新调整为原始尺寸。通过采用均方根误差将结果与常用的焦点度量算子进行比较（RMSE），相关性和图像质量指数（ Q）。与其他方法相比，所提出的使用DNN的SFF方法显示出更高的精度和更低的计算时间消耗。