Focus stacking is widely used in micro, macro, and landscape photography to reconstruct all-in-focus images from multiple frames obtained with focus bracketing, that is, with shallow depth of field and different focus planes. Existing deep learning approaches to the underlying multi-focus image fusion problem have limited applicability to real-world imagery since they are designed for very short image sequences (two to four images), and are typically trained on small, low-resolution datasets either acquired by light-field cameras or generated synthetically. We introduce a new dataset consisting of 94 high-resolution bursts of raw images with focus bracketing, with pseudo ground truth computed from the data using state-of-the-art commercial software. This dataset is used to train the first deep learning algorithm for focus stacking capable of handling bursts of sufficient length for real-world applications. Qualitative experiments demonstrate that it is on par with existing commercial solutions in the long-burst, realistic regime while being significantly more tolerant to noise. The code and dataset are available at https://github.com/araujoalexandre/FocusStackingDataset.

翻译：聚焦堆叠广泛应用于微距、宏观及风景摄影中，通过景深较浅且聚焦平面不同的多帧包围曝光图像，重建全清晰图像。现有的多焦点图像融合深度学习方法在真实图像中的应用有限，因为它们针对极短图像序列（2-4幅图像）设计，且通常采用光场相机采集或合成生成的小规模低分辨率数据集进行训练。我们提出一个包含94组高分辨率原始包围曝光图像的新数据集，并利用最新商业软件从数据中计算得到伪真实标签。该数据集用于训练首个能处理真实世界应用所需足够长序列的聚焦堆叠深度学习算法。定性实验表明，在长序列真实场景下，该算法与现有商业解决方案性能相当，且对噪声具有显著更高的容忍度。代码与数据集详见https://github.com/araujoalexandre/FocusStackingDataset。