Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera. In this work, we propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV). By computing the first-order derivative with the stacked features over different focal distances, DFV is able to capture both the focus and context information for focus analysis. Besides, we also introduce a probability regression mechanism for focus estimation to handle sparsely sampled focal stacks and provide uncertainty estimation to the final prediction. Comprehensive experiments demonstrate that the proposed model achieves state-of-the-art performance on multiple datasets with good generalizability and fast speed.

本文提出了一种基于卷积神经网络的深度学习方法，使用微调的焦距图像识别出最佳焦点像素并通过焦距估计推断深度，通过深度差分聚合光学信息，利用概率回归处理稀疏采样的数据集，对目标的深度估计及预测提供不确定性。实验结果表明，该模型在多个数据集上均能取得最佳性能，具有很好的普适性和快速性能。

差分聚焦体的深度自聚焦技术